UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-identiﬁcation

in Video Imagery

Daniel Organisciak

, Matthew Poyser

, Aishah Alsehaim

, Shanfeng Hu

, Brian K. S. Isaac-Medina

Toby P. Breckon

and Hubert P. H. Shum

2,∗

Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, U.K.

Department of Computer Science, Durham University, Durham, U.K.

Keywords:

Drone, UAV, Re-ID, Tracking, Deep Learning, Convolutional Neural Network.

Abstract:

As unmanned aerial vehicles (UAV) become more accessible with a growing range of applications, the risk of

UAV disruption increases. Recent development in deep learning allows vision-based counter-UAV systems to

detect and track UAVs with a single camera. However, the limited ﬁeld of view of a single camera necessitates

multi-camera conﬁgurations to match UAVs across viewpoints – a problem known as re-identiﬁcation (Re-

ID). While there has been extensive research on person and vehicle Re-ID to match objects across time and

viewpoints, to the best of our knowledge, UAV Re-ID remains unresearched but challenging due to great

differences in scale and pose. We propose the ﬁrst UAV re-identiﬁcation data set, UAV-reID, to facilitate the

development of machine learning solutions in multi-camera environments. UAV-reID has two sub-challenges:

Temporally-Near and Big-to-Small to evaluate Re-ID performance across viewpoints and scale respectively.

We conduct a benchmark study by extensively evaluating different Re-ID deep learning based approaches and

their variants, spanning both convolutional and transformer architectures. Under the optimal conﬁguration,

such approaches are sufﬁciently powerful to learn a well-performing representation for UAV (81.9% mAP for

Temporally-Near, 46.5% for the more difﬁcult Big-to-Small challenge), while vision transformers are the most

robust to extreme variance of scale.

1 INTRODUCTION

Unmanned aerial vehicles (UAV) are becoming more

accessible and more powerful through technological

advancement. Their small size and manoeuvrabil-

ity allows for a wealth of applications, such as ﬁlm-

making, search and rescue, infrastructure inspection,

and landscape surveying. However, the malicious or

accidental use of UAVs could pose a risk to aviation

safety systems or privacy. This necessitates the de-

velopment of counter-UAV systems. Due to the re-

cent development of computer vision and deep learn-

ing, vision-based UAV detection and tracking systems

have become more robust and reliable (Isaac-Medina

et al., 2021; Jiang et al., 2021).

There are two major issues with existing vision-

based counter-UAV systems: ﬁrstly, many systems

are only built for a single camera – once a UAV

leaves the range of capture, the captured information

can no longer be re-used; secondly, to help prevent

ID-switching and handle occlusion, many tracking

∗

Corresponding author

frameworks rely on a generic re-identiﬁcation (Re-

ID) module, which cannot comprehensively handle

the complex challenges that come with re-identifying

UAVs (Isaac-Medina et al., 2021).

Of these, DeepSORT (Wojke et al., 2017) and

Tracktor (Bergmann et al., 2019) are perhaps the two

most prominent frameworks within the tracking do-

main. Tracktor requires the network to associate new

and previously disassociated tracks. DeepSORT on

the other hand, employs its Re-ID module at each

time step within the Hungarian Algorithm (Kuhn,

2012) to associate new and old detections. Indeed, in

the original and many subsequent works, the associa-

tion metric is heavily weighted towards the output of

the Re-ID network, especially when camera motion is

particularly prevalent. The reliance upon robust rei-

dentiﬁcation networks by both single and multi-view

tracking frameworks is evident and thus dedicated

study to effectively re-identify UAVs is essential to

solve both problems. To enable a cross-camera UAV

system, effective Re-ID is needed to match observed

UAVs from one camera to another from different an-

136

Organisciak, D., Poyser, M., Alsehaim, A., Hu, S., Isaac-Medina, B., Breckon, T. and Shum, H.

UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-identiﬁcation in Video Imagery.

DOI: 10.5220/0010836600003124

In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP, pages

136-146

ISBN: 978-989-758-555-5; ISSN: 2184-4321

Big-to-Small

Temporally-Near

Figure 1: The two Re-ID sub-challenges we explore.

Temporally-Near models the difﬁculties of tracking UAVs,

whereas Big-to-Small simulates cross-camera or temporally

distant challenges of matching UAVs.

gles, poses, and scales. Generic Re-ID mechanisms

within off-the-shelf tracking frameworks can be im-

proved by designing a bespoke UAV Re-ID system to

handle these extreme changes.

There has been a large body of research in Re-

ID for pedestrians (Ye et al., 2021) and vehicles

(Deng et al., 2021). Most state-of-the-art person Re-

ID research typically employ engineering solutions

to improve performance, such as a ‘bag of tricks’

(Luo et al., 2019), which identiﬁes several key Re-

ID principles to adhere to. Indeed, such methods

have been illustrated to introduce sufﬁcient robust-

ness such that state of the art results for person Re-ID

can be achieved, even by shallow networks (Breckon

and Alsehaim, 2021). Other works exploit the rela-

tively static colour proﬁle of pedestrians across views

with part-based systems (Sun et al., 2018; Fu et al.,

2019). In contrast, vehicles have drastically different

appearances across views, so this information must be

incorporated into the model (Zhou and Shao, 2018).

For UAV Re-ID, even more consideration is required

due to the increased potential for the change in view-

ing angle of the UAV target from any given camera

position owing to their unconstrained motion in 3D

space.

As a result of their unconstrained aerial motion

UAV may undergo considerably greater changes in

scale relative to the camera than comparable pedes-

trian or vehicle targets. Furthermore, they can appear

from any angle on the sphere, compared to pedestri-

ans and vehicles, that are typically captured from a

0-30°elevation. As a result of these extended inter-

view object tracking challenges, a study is required to

evaluate the performance of existing Re-ID systems

on these challenges that UAVs provide.

However, to the best of our knowledge, there

has been no research on UAV Re-ID. In the ab-

sence of a true multi-view UAV data set, we pro-

pose the UAV-reID dataset, as a new and challeng-

ing benchmark for UAV Re-ID. To simulate Re-ID

challenges, UAV-reID has two sub-challenge dataset

splits: Temporally-Near aims to evaluate the perfor-

mance across a short time distance, as Re-ID mod-

ules within tracking frameworks must successfully

identify the same UAV in subsequent frames within

videos; Big-to-Small evaluates Re-ID performance

across large scale differences. The results inform Re-

ID performance of matching UAVs across two cam-

eras, or across a large timescale within the same cam-

era. Figure 1 visualises these sub-challenges.

We conduct a benchmark study of state-of-the-art

deep neural networks and frameworks designed for

Re-ID, including ResNet (He et al., 2016), SE-ResNet

(Hu et al., 2018), SE-ResNeXt (Xie et al., 2017), Vi-

sion Transformers (ViT) (Dosovitskiy et al., 2021),

ResNetMid (Yu et al., 2017), Omni-scale Network

(OSNet) (Zhou et al., 2019), Multi-level Factorisation

Network (MLFN) (Chang et al., 2018), Parts-based

Convolutional Baseline (PCB) (Sun et al., 2018),

Harmonious Attention Network (HACNN) (Li et al.,

2018), and Not 3D Re-ID (N3D-ReID) (Breckon and

Alsehaim, 2021). We test all baselines with a cross-

entropy loss, a triplet loss, a combined loss and a

multi-loss.

Experimental results show that existing Re-ID

networks cannot transfer seamlessly to UAV Re-ID,

with the best setup achieving 81.9% mAP under

Temporally-Near and 46.5% under Big-to-Small. ViT

is the most robust to extreme scale variance. This

compares to 84.61% (Breckon and Alsehaim, 2021)

performance when evaluated on typical pedestrian or

vehicle targets (e.g. MARS dataset (Zheng et al.,

2016)) as are commonplace in existing Re-ID eval-

uation benchmarks.

The contributions of this paper are summarised as

follows:

• proposal of the novel task of UAV Re-ID to match

UAVs across cameras and time frames, to improve

visual security solutions on UAVs

• construction of the ﬁrst UAV Re-ID data set

UAV-reID, to facilitate Re-ID system development

and benchmarking. This is formulated by two

sub-challenge dataset splits, Temporally-Near and

Big-to-Small, to evaluate performance under con-

ditions where Re-ID is used in a practical environ-

ment, and remain applicable even when dataset

availability is constrained.

• creation of the ﬁrst extensive benchmark over

UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-identiﬁcation in Video Imagery

137

a variety of state-of-the-art Re-ID architectures

within the UAV domain: ResNet, SE-ResNet, SE-

ResNeXt, ViT, ResNetMid, OSNet, MLFN, PCB,

HACNN, N3D-ReID; with critical evaluation of

their strengths and weaknesses, obtaining 81.9%

mAP on Temporally-Near and 46.5% mAP on

Big-to-Small.

2 RELATED WORK

Here we detail existing literature with respect to evo-

lution of Re-ID methodology, and its application

within the UAV domain.

2.1 Re-identiﬁcation

Before large-scale Re-ID data sets were proposed,

traditional machine learning works focused on de-

signing hand-crafted features and learning distance

metrics (Karanam et al., 2019). Even though UAV-

reID is a small data set, UAVs can appear at many

different sizes and it is difﬁcult to hand-craft fea-

tures that are robust to this extreme scale transfor-

mation. For this reason, we conduct this study on

deep learning methods which are capable of comput-

ing robust features (He et al., 2016; Hu et al., 2018)

and demonstrate supreme performance on other Re-

ID tasks (Sun et al., 2018; Hermans et al., 2017; Li

et al., 2018).

Re-ID with deep learning became popular after

the release of ResNet (He et al., 2016) with many

works taking advantage of the complex information

that very deep features could encode. More recently,

extensions such as SE-ResNet (Hu et al., 2018) and

SE-ResNeXt (Xie et al., 2017) have seen more use

as a generic backbone architecture for Re-ID frame-

works. These frameworks commonly consist of engi-

neering solutions (Luo et al., 2019) for easier repre-

sentation matching. Person Re-ID (Ye et al., 2021)

frameworks typically take advantage of the similar

colour proﬁle of pedestrians across views, often by

splitting the image into parts (Sun et al., 2018; Fu

et al., 2019) to separately encode information of the

head, clothes, and shoes. Conversely, vehicle Re-ID

(Deng et al., 2021) has to contend with shape infor-

mation that undergoes signiﬁcant deformation across

viewpoints, which may require encoding viewpoint

information within the model (Zhou and Shao, 2018;

Meng et al., 2020).

Compared to most classiﬁcation problems, Re-

ID often contains many classes (individuals, vehicles,

UAVs) and few samples per class. This makes learn-

ing class-speciﬁc features difﬁcult. To handle this

problem, it is often beneﬁcial to consider metric learn-

ing, usually in the form of the triplet loss (Hoffer and

Ailon, 2015) or centre loss (Wen et al., 2016). The

triplet loss in particular has seen extensive use for per-

son (Hermans et al., 2017; Cheng et al., 2016) and ve-

hicle (Kuma et al., 2019) Re-ID, and can even handle

both tasks simultaneously (Organisciak et al., 2020).

Within this study it is therefore natural to consider the

triplet loss for UAV Re-ID.

2.2 Computer Vision on UAV

A large body of research applying computer vi-

sion to imagery captured by UAVs has been devel-

oped, including object detection (Gaszczak et al.,

2011), visual saliency detection (Sokalski et al., 2010;

okstorp and Breckon, 2021), visual segmentation

(Lyu et al., 2020), target tracking (Li et al., 2020)

and aerial Re-ID (Grigorev et al., 2019; Teng et al.,

2021; Zhang et al., 2021). However, the study of

such tasks where UAV are the main object of inter-

est has not been extensively investigated. Most UAV-

related computer vision research is focused on deep

learning approaches for UAV detection and tracking

(Isaac-Medina et al., 2021; Liu et al., 2020b; Craye

and Ardjoune, 2019; Opromolla et al., 2019). In this

context, some data sets have been created to investi-

gate novel visual-based counter-UAV systems. The

Drone-vs-Bird Challenge data set (Coluccia et al.,

2019) collects a series of videos where UAV usu-

ally appear small and can be easily confused with

other objects, such as birds. Recently, the Anti-UAV

data set (Jiang et al., 2021) has been proposed to

evaluate several tracking algorithms in both optical

and infrared modalities. Despite the advances in the

counter-UAV domain and the available data sets, this

study represents the ﬁrst time UAV Re-ID has been in-

vestigated. We believe this is a crucial task for future

vision-based counter-UAV systems, which are both

passive in nature and, of course, afford visual con-

ﬁrmation of acquired UAV targets.

3 DEEP NEURAL NETWORK

ARCHITECTURES

We present an overview of the deep learning archi-

tectures considered within this work in terms of both

their underlying convolutional neural network back-

bone and the loss function that they employ for weight

optimisation.

VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications

138

Feature Representation

Softmax Output

Triplet Loss Cross-Entropy Loss

Backbone Network

Input Data

Figure 2: An overview of the pipeline for all of our experiments. Input data from the proposed UAV-ReID data set is processed

by the given backbone network to obtain a feature representation. This feature representation is used in the triplet loss, and

also goes through a softmax classiﬁcation layer to be used in the cross-entropy loss. The backbone networks we evaluate are

presented in Section 3.1.

3.1 Network Backbones

Deep neural networks (DNN) are machine learning

systems that use multiple layers of non-linear compu-

tation to model the complicated relationship between

the input and output of a problem. Convolutional neu-

ral networks (CNN) are particularly suited for image-

based object identiﬁcation and tracking in computer

vision applications. Firstly, CNNs can capture object

features irrespective of their spatial locations on an

image, due to the shift-invariance of convolution ker-

nels. Secondly, modern CNNs can detect objects of

complex shapes, sizes, and appearance by stacking

multiple convolution kernels to learn powerful fea-

ture representations. We describe a selection of state-

of-the-art CNNs and generic Re-ID frameworks that

we evaluate for UAV Re-ID. Our overall framework

is shown in Figure 2.

ResNet: Residual neural networks (He et al., 2016)

are a popular variant of CNNs that connect adjacent

layers of a network (residuals) with an identity map-

ping. Learning residuals enables training signiﬁcantly

deeper architectures to obtain more powerful features.

In our experiments, we use the 18-layer, 34-layer, and

50-layer conﬁgurations.

SE-ResNet: ResNets are powerful but can still be im-

proved by learning and re-weighting the hidden con-

volutional feature maps using attention. The popu-

lar Squeeze-Excitation (SE) network (Hu et al., 2018)

introduces a channel attention mechanism to identify

and appropriately weight important feature maps.

SE-ResNeXt: Another line of improvement for

ResNet is ResNeXt (Xie et al., 2017), which main-

tains the identity skip connection while splitting the

feature mapping of each layer into multiple branches.

This increased dimension of network representation

power has shown to be more effective for image

recognition and object detection.

ViT: Transformers have recently become ubiquitous

in natural language processing. Motivated by this,

Dosovitskiy et al. (Dosovitskiy et al., 2021) migrated

transformers into computer vision to propose Vision

Transformers (ViT). This architecture learns the re-

lationship among all image patches for downstream

tasks. We evaluate ViT with image patches of size

16 × 16 with the ‘small’ (8-layer) and ‘base’ (12-

layer) conﬁgurations.

ResNet50-mid: A common practice of image repre-

sentation learning in computer vision is to take hid-

den features from the penultimate CNN layer as im-

age embeddings. Yu et al. (Yu et al., 2017) explore

fusing embeddings from earlier layers to improve the

performance of cross-domain image matching. Fus-

ing representations from different layers has proven

successful for other computer vision tasks on small

objects (Liu et al., 2020a), highlighting its potential

within UAV Re-ID systems.

OSNet: There have also been CNN architectures

speciﬁcally designed for object Re-ID. Zhou et al.

(Zhou et al., 2019) propose an omni-scale network,

which improves Re-ID performance by learning to

fuse features of multiple scales within a residual con-

volutional block. Each stream in the block corre-

sponds to one scale to learn and the outputs of all

streams are dynamically combined to create omni-

scale features. Considering the expansive array of

scales at which UAV can appear, OSNet is well-suited

to the UAV Re-ID challenge.

MLFN: Multi-level Factorisation Network (Chang

et al., 2018) is similar to OSNet in that it tries to

capture discriminative and view-invariant features at

multiple semantic levels. Unlike OSNet however, it

composes multiple computational blocks, each con-

taining multiple factor modules and a selection gate

to dynamically choose the best module to represent

the input.

PCB: Different from holistic feature learning, Sun et

al. (Sun et al., 2018) propose a parts-based convo-

lutional baseline (PCB), which uniformly splits each

input image into multiple parts. As the appearance

consistency within each part is usually stronger than

between parts, it proves easier to learn more robust

UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-identiﬁcation in Video Imagery

139

and discriminative features for person Re-ID. A part

pooling module is added to deal with outliers.

HACNN: Li et al. (Li et al., 2018) propose a harmo-

nious attention network, which tackles the challenge

of matching persons across unconstrained images that

are potentially not aligned. HACNN uses layers that

incorporate hard attention, spatial attention and chan-

nel attention to improve person Re-ID performance

on unconstrained images. We reformulate this system

towards re-identiﬁcation of UAV objects to thus en-

able evaluation of its performance within the counter-

UAV domain

The N3D-ReID Framework: The use of Re-ID best

practices (Luo et al., 2019) alongside simple networks

have been demonstrated to be a suitable replacement

for more complex Re-ID networks, as identiﬁed by

the Not 3D Re-ID Framework (Breckon and Alse-

haim, 2021) (N3D-ReID). By introducing a Batch

Normalisation Neck between the deep backbone net-

work and a multi-loss function explained in Section

3.2, the authors were able to achieve state of the art re-

sults within the person Re-ID domain. Moreover, they

utilize an additional backbone architecture denoted

ResNet50-IBN-a (Pan et al., 2018), which introduces

both batch normalisation (Ioffe and Szegedy, 2015)

and instance normalisation (Ulyanov et al., 2017) into

the backbone architecture itself. As such, we fur-

ther evaluate the performance of ResNet50-IBN-a and

the backbone architectures outlined in Section 3.1

within this separate re-identiﬁcation framework in ad-

dition to that illustrated in Figure 2. All implementa-

tion details remain unchanged from the original paper

(Breckon and Alsehaim, 2021).

3.2 Loss Functions

In order to perform learning via weight optimisation

across the speciﬁed deep neural network architecture,

a loss function denoting relative network weight per-

formance on the speciﬁed task is minimised via com-

putational optimisation with corresponding weight

updates via backpropagation. We detail a number of

such loss functions which are considered within this

study for the application of UAV Re-ID.

Cross-Entropy Loss: The cross-entropy (CE) loss

function is the standard loss that is used in most ma-

chine learning classiﬁcation tasks. The negative log-

likelihood between the true class labels and predicted

class labels is minimised:

= −

∑

x∈X

log f (x; θ), (1)

where a network f with parameters θ predicts the

class of an input x with a true class index y

Triplet Loss: The triplet loss is a metric learning

technique that decreases the distance between positive

pairs of images and increases the distance of negative

pairs. Metric learning is commonly used in applica-

tions such as veriﬁcation and Re-ID, where there are

many classes and few instances per classes. Because

of the lack of class-speciﬁc data, the network cannot

reliably learn class-speciﬁc information. The network

instead learns to place images onto a manifold with

similar images placed close to one another.

We denote a triplet, t = (x, x

, x

−

), where x is the

query image, x

is an image of the same object, and

−

is an image of a different object. The triplet loss

function is formulated as follows:

triplet

∑

t∈T

max((|| f

∗

(x;θ) − f

∗

;θ)||

− || f

∗

(x;θ) − f

∗

−

;θ)||

+ α), 0),

(2)

where T is the set of mined triplets, || · ||

is the

Euclidean distance, and the feature representation

∗

(x;θ) is obtained by passing input x through net-

work f with parameters θ, and taking the representa-

tion before the softmax classiﬁcation layer. Negative

images are pushed away from positive images by a

margin of α.

Triplets need to be sufﬁciently difﬁcult in order

to improve the performance of the model (Hermans

et al., 2017). We employ hard negative mining to

each query image in the batch. This means that within

each iteration, the most difﬁcult negative samples are

considered and processed by the loss function. In

turn, these samples maximise how much is learnt dur-

ing backpropagation. Given a query image q, the

hardest negative image in the gallery is found via

min|| f

∗

(q)− f

∗

)||

, where g

, i ∈ {1, . . . , B} are the

gallery images, B is the batch size, and ||·||

is the Eu-

clidean distance.

Combined Loss: In many Re-ID works, combining

the two losses can lead to performance gains (Luo

et al., 2019). We test this setting for UAVs where both

losses receive equal weight:

L = L

+ L

triplet

. (3)

Multi-loss: Following the success of N3D-ReID

(Breckon and Alsehaim, 2021), we further evalu-

ate the performance of a multi-loss function that

has demonstrated superior performance to more well-

established loss functions within the person Re-ID

domain. This loss is formulated as a weighted sum

across cross-entropy loss, L

, ranked list loss, L

RLL

centre loss, L

centre

, and erasing-attention loss, L

E att

as follows:

L = L

+ L

RLL

+ β · L

centre

+ L

E att

. (4)

VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications

140

Query

Rank

4 5

Figure 3: Examples from ViT with a combined loss on Big-

to-Small. A green box indicates a correct Re-ID. ViT can

extract salient features from very low-resolution images to

match UAVs across scale.

As such, all losses receive equal weighting other

than centre loss which serves to support L

RLL

, and

thus receive weight β. We deﬁne L

as cross-entropy

loss with additional Label Smoothing (Szegedy et al.,

2016). L

RLL

can be considered a direct alternative to

triplet loss, and learns a hypersphere for each class

additionally to triplet loss behaviour. Learning the hy-

persphere helps avoid intra-class data distribution that

might be apparent within triplet loss, and particularly

impactful when training with limited data. Finally,

E att

introduces additional attention to image sam-

ples that receive erasing under random erasing aug-

mentation (Zhong et al., 2020) such that its impact

is increased, as implemented in (Breckon and Alse-

haim, 2021; Pathak et al., 2020). This is particularly

important when data availability is constrained so the

effects of over-ﬁtting are minimised during training;

learning will be maximised from features extracted

from erasing-augmented images that are less likely to

contribute to UAV regions.

4 UAV Re-ID DATASET

We present our dataset for the UAV Re-ID task and

corresponding experimental setup.

4.1 Data

UAV-reID is designed to evaluate two practical ap-

plications of Re-ID. All data set instances are con-

structed via sampling from 61 UAV videos. UAVs

are cropped from single frames of these videos de-

pending on the speciﬁc challenge. UAV images are

then resized to size 224 × 224 Images are augmented

via random ﬂipping, random cropping, and random

erasing (Zhong et al., 2020). Similar to early person

Re-ID data sets, we include two images per identity

for each setting. Across both challenges, our dataset

contains 61 UAV identities and 244 UAV images.

We use 30 identities for training and the remaining

31 identities for testing. Our code can be found at

https://github.com/danielorganisciak/UAVReID.

4.2 Challenges

Temporally-Near: Given a UAV video with t frames,

we consider UAVs in frames

and

. This temporal

distance is close enough that UAVs remain at a sim-

ilar size in most cases, but far enough for UAVs to

appear from a different viewpoint. This simulates the

task that a Re-ID module embedded within a tracking

framework must perform, whereby UAVs undergo a

limited transformation.

Big-to-Small: We obtain the largest and smallest

UAV detections across the whole video. This simu-

lates the task of matching known UAVs (for which

we have rich visual information) with UAVs detected

from a long distance. As such, we can identify the

far-off UAV, and whether it poses a potential threat.

4.3 Evaluation Protocol

We use the standard mean average precision (mAP),

and rank based metrics to evaluate the selected state-

of-the-art methods. The test set is split into a query

set and a gallery set, with 31 identities each. Given a

query image, q, the Re-ID framework ranks all gallery

images, g

in order of likelihood that g

= q, i.e. they

contain the same UAV.

The rank-r matching rate is the percentage of

query images with a positive gallery image within the

highest r ranks. The precision at rank r, P

, compares

the number of true positives (TP) with the total num-

ber of positives in the top r ranks:

TP+FP

, (5)

where FP is the number of false positives. As we only

have one gallery image per query image, the mAP is

calculated via

mAP =

∑

q=1

, (6)

where the correct identity of q is found at rank r

, and

Q is the total number of query images.

All experiments were performed using the

torchreid framework (Zhou and Xiang, 2019) on an

NVIDIA RTX 2080 Ti GPU. All backbones were pre-

trained on ImageNet.

UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-identiﬁcation in Video Imagery

141

Table 1: Methods Tested on the ‘Temporally-Near’ sub-challenge.

Backbone Re-ID CE Triplet CE + Triplet

mAP rank-1 rank-5 mAP rank-1 rank-5 mAP rank-1 rank-5

ResNet-18 × 81.9 77.4 77.4 72.7 61.3 74.2 71.7 58.0 77.4

ResNet-34 × 77.1 70.1 74.2 74.6 71.0 71.0 74.4 61.3 83.9

ResNet-50 × 75.9 71.0 71.0 75.5 71.0 71.0 76.7 67.7 77.4

SE-ResNet-50 × 77.1 71.0 80.6 74.1 67.7 74.2 79.4 74.2 80.6

SE-ResNeXt-50 × 75.8 71.0 77.4 66.8 61.3 64.5 76.2 74.2 74.2

ViT Small × 75.6 67.7 74.2 74.1 64.5 74.2 75.6 64.5 74.2

ViT Base × 79.2 74.2 77.4 73.2 67.7 74.2 81.3 77.4 80.6

ResNet50mid X 78.0 71.0 87.1 74.0 67.7 74.2 76.1 67.7 77.4

OSNet X 71.0 61.3 70.1 73.8 67.7 71.0 75.7 71.0 71.0

MLFN X 69.9 61.3 71.0 73.4 67.7 67.7 65.7 58.1 61.3

PCB X 80.8 74.2 87.1 73.2 67.7 67.7 81.4 77.4 80.6

HACNN X 72.1 64.5 71.0 77.7 71.0 77.4 74.5 64.5 77.4

Bold denotes the highest values in the table, red denotes the highest in each column, blue denotes the second highest in each column.

Table 2: Methods Tested on the ‘Big-to-Small’ sub-challenge.

Backbone Re-ID CE Triplet CE + Triplet

mAP rank-1 rank-5 mAP rank-1 rank-5 mAP rank-1 rank-5

ResNet-18 × 40.3 32.3 41.9 36.9 25.8 32.3 37.5 25.8 32.3

ResNet-34 × 33.7 22.6 29.0 37.9 29.0 35.5 38.8 25.8 35.5

ResNet-50 × 37.8 22.6 51.6 39.0 29.0 35.5 42.9 29.0 35.5

SE-ResNet-50 × 38.0 25.8 51.6 42.5 29.0 45.0 41.4 29.0 38.7

SE-ResNeXt-50 × 40.0 29.0 35.5 31.9 16.1 29.0 38.8 29.0 32.3

ViT Small × 43.1 35.5 35.5 39.0 22.6 41.9 40.9 29.0 38.7

ViT Base × 40.5 29.0 54.8 36.9 22.6 32.3 46.5 35.5 45.2

ResNet50mid X 38.4 25.8 51.6 42.3 32.3 32.3 43.2 32.3 38.7

OSNet X 38.0 25.8 35.5 34.5 19.4 35.5 33.2 19.4 32.3

MLFN X 38.1 22.5 38.7 36.8 25.8 32.3 33.9 22.6 25.8

PCB X 41.3 32.3 35.5 43.7 32.3 41.9 38.2 25.8 32.3

HACNN X 36.0 19.4 45.2 39.4 25.8 32.3 41.2 25.8 41.9

Bold denotes the highest values in the table, red denotes the highest in each column, blue denotes the second highest in each column

5 EVALUATION

We conduct an extensive benchmark evaluation

over both the Temporally-Near and Big-to-Small re-

identiﬁcation challenges.

5.1 Results

Results on the ‘Temporally-Near’ and ‘Big-to-Small’

sub-challenge dataset splits can be found in Table

1 and 2, respectively. ViT Base with CE+Triplet

loss comprehensively outperforms all other methods

on the Big-to-Small sub-challenge, and has fourth

highest mAP on the Temporally-Near sub-challenge.

From Figure 3, rows two and three, we observe that

ViT returns a similar ranking list on query UAVs that

have different colour. It follows that ViT is captur-

ing shape information as well as colour, which we

hypothesise is due to its global self-attention mech-

anism, yielding superior performance compared to

convolutional methods that rely on a local receptive

ﬁeld. This is in-keeping with the results of (Isaac-

Medina et al., 2021), which corroborates the suit-

ability of transformer networks towards detecting and

identifying small objects such as drones. Similar to

ViT, PCB also splits the input image into parts and

obtains good performance across both tasks. This in-

dicates that a part-based strategy can be effective for

UAV Re-ID.

As expected, Big-to-Small is more challenging

than Temporally-Near due to the extreme variation

in scale. The best rank-1 matching rate of 77.4%

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Table 3: Methods Tested Using the N3D-ReID framework (Breckon and Alsehaim, 2021)

Backbone Temporally-Near Big-to-Small

mAP rank-1 rank-5 mAP rank-1 rank-5

ResNet-18 74.3 67.7 71.0 36.4 25.8 29.0

ResNet-34 70.1 64.5 67.7 37.8 29.0 32.3

ResNet-50 79.5 74.2 77.4 38.5 29.0 32.3

SE-ResNet-50 72.1 64.5 71.0 40.2 32.3 35.5

SE-ResNeXt-50 72.0 67.7 67.7 39.4 29.0 35.5

ViT Small 79.2 71.0 77.4 39.6 29.0 32.3

ViT Base 77.0 71.0 77.4 41.6 29.0 38.7

ResNet50mid 78.7 71.0 77.4 45.6 35.5 41.9

OSNet 81.5 77.4 80.7 35.2 22.6 29.0

MLFN 74.3 67.7 71.0 40.8 32.3 41.9

PCB 80.5 74.2 80.7 39.3 29.0 32.3

HACNN 74.1 67.7 74.2 41.6 32.3 35.5

IBN-A 72.0 64.5 67.7 41.9 32.3 35.5

Red denotes the (joint) highest in each column, blue denotes the (joint) second highest in each column

from generic architectures such as ResNet-18 and ViT

is a strong baseline under the Temporally-Near sub-

challenge. For real-world tracking systems, Re-ID is

performed with only a few possible matches, rather

than the entire test data set. These methods should

therefore be sufﬁciently strong to be immediately em-

ployed within real-world systems.

In contrast, Big-to-Small has top rank-1 and rank-

5 matching rates of just 35.5% and 54.8%, respec-

tively. We can attribute the difﬁculty of the challenge

to the reduced colour and structure detail available to

networks at a small scale, limiting the number of a dif-

ferentiating features to identify. While colour exists,

‘blocky’ compression artifacts are much more preva-

lent and there is very little variation across the im-

age. As such, networks must be capable of identify-

ing UAV from low-quality shape information, which

only a few networks are capable of doing at this scale.

Although ViT demonstrates potential in this regard,

this sub-challenge requires further research to develop

UAV-speciﬁc architectures sufﬁciently robust to scale

and pose, and thus able to identify far away UAV.

The networks speciﬁc to Re-ID generally do not

perform as well as generic networks. One reason for

this is that extensive hyperparameter tuning is per-

formed on generic networks to maximise classiﬁca-

tion performance on ImageNet, with a huge variety of

objects seen. ReID-speciﬁc networks, although pre-

trained on ImageNet, tune hyperparameters to max-

imise performance on person Re-ID data sets. Hav-

ing specialised on humans, they have less functional

ability to be transferred to different objects. However,

PCB, which uses a ResNet-50 backbone (optimised

for ImageNet), does still attain strong performance.

In almost all cases, cross-entropy loss perfor-

mance exceeds triplet loss. Further, the combined loss

is occasionally unable to yield higher performance

than cross-entropy alone. It is a common occurrence

however, that triplet loss performance improves as

the number of classes within the data set increases.

Furthermore, because UAV-reID only allows one-to-

one matching, we cannot harness the power of hard-

positive mining. We expect that triplet loss will gen-

erate better results, and perhaps exceed cross-entropy,

when a more comprehensive data set is made avail-

able.

The results from the Not-3D Re-ID framework

(Table 3) corroborate our ﬁndings. Indeed, the addi-

tional loss functions incorporated into one multi-loss

aggregation function are generally unable to improve

results, but instead offer comparable results (+/-1%)

over the earlier loss formulations (Table 1, 2). This

is again perhaps attributable to the lack of effective

hard-positive mining and few available classes. We

can once again conclude that complex state-of-the-

art person re-identiﬁcation networks are less suited

to UAV re-identiﬁcation than shallower, simpler net-

works. In this regard, we can ﬁrstly observe that the

IBN-A network does not out-perform the other net-

works in either challenge. The mAP performance of

IBN-A under the temporally-near challenge (72.0%)

is signiﬁcantly inferior to other backbone architec-

tures. Secondly, the N3D-ReID framework is only

able to improve upon ResNet50 (79.5% mAP over

76.7% mAP) and ViT Small (79.% mAP over 75.6%

mAP) generic re-identiﬁcation networks. However,

N3D-ReID yields consistently stronger results for the

Re-ID speciﬁc networks under the Temporally-Near

challenge, with the exception of HACNN (74.1%

mAP compared to 77.7% mAP).

UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-identiﬁcation in Video Imagery

143

Figure 4: Attention visualisation of the transformer mech-

anism within ViT on the Big-to-Small setting. Attention

from four different heads of the CLS token is presented.

Different attention heads attend to different parts of the im-

age, forming a more robust feature representation.

Overall, OSNET performs the strongest with the

N3D-ReID conﬁguration, achieving 81.5%. How-

ever, this does not improve upon ResNet-18 with

just cross entropy loss (81.9%, Table 1). Any

improvements upon the Big-to-Small challenge re-

sults are similarly negligible when employing N3D-

ReID. ResNet50mid generates the highest mAP of

45.6% in this regard, less than that of ViT Base,

46.5%, when using a combination of only cross en-

tropy and triplet loss. Nevertheless, the results are

further indicative that networks that achieve good

results on the Temporally-Near challenge are not

necessarily well-suited for the Big-to-Small chal-

lenge; the best performing networks under the N3D-

ReID framework for Temporally-Near (OSNet, PCB,

ResNet50) are disjoint from those suited to Big-to-

Small (ResNet50mid, ViT Base, MLFN).

5.2 Interpreting Vision Transformers

Across all experiments, ViT attains the highest per-

formance on the Big-to-Small challenge with 46.5%

mAP. We visualise the attention maps to get a better

understanding of how they achieve this. Figure 4 is

a visualisation of four different attention heads of the

CLS token.

The ﬁrst attention map attends to the entire UAV,

the second attends to its legs, the third to the pro-

pellers and the top of the UAV. This demonstrates

clearly how it is encoding features and what the ﬁnal

feature representation consists of. The fourth atten-

tion map isolates the background. Even though the

background is complicated, the attention head identi-

ﬁes that the drone is the foreground object, and con-

siders the clouds and the trees together. This gives

conﬁdence that ViT has a good understanding of the

image, and that the feature representation is com-

posed in a structurally sound manner.

Figure 5 visualises attention from a speciﬁc im-

age patch, indicated via the yellow box. On the left,

the query patch occurs on the UAV, and the resulting

attention strongly segments the UAV from the back-

ground. On the right, the query patch occurs on one

of the propellers, and the attention head attends to

each of the other propellers. One of the advantages

Figure 5: Attention visualisation of the transformer mech-

anism within ViT on the Big-to-Small setting. The query

patch is indicated by a yellow square.

of transformers over traditional convolution is their

ability to learn non-local relationships between im-

age patches to obtain a stronger feature representa-

tion. These visualisations demonstrate this process in

action.

6 CONCLUSIONS

We have proposed the challenge of UAV re-

identiﬁcation and performed a benchmark study to

examine the effectiveness of a variety of deep learn-

ing techniques. Vision transformers trained with a

combined cross-entropy and triplet loss attain strong

performance across both tasks, achieving the highest

mAP on the Big-to-Small challenge and the 4th high-

est mAP on the Temporally-Near setting. A range of

methods can re-identify UAVs over a short time pe-

riod with high precision. Of these methods, ResNet-

18 (mAP 81.9 %) appears to be easiest to ﬁt into track-

ing frameworks due to its high performance and rela-

tively small model size.

Although the Big-to-Small data set split is very

challenging, vision transformers have shown great

promise with respect to handling extreme scale trans-

formation. We can attribute this behaviour to their

superior performance over other architectures due to

their ability to learn relationships between distant im-

age patches.

There is clear motivation for future work. A large

multi-view UAV Re-ID data set with more instance

classes would be beneﬁcial to get the full potential out

of deep networks and multiple loss functions. Based

on its success in this benchmark, we also wish to de-

velop an improved vision transformer by incorporat-

ing techniques used in convolutional neural networks

to handle scale changes, such as concatenating out-

puts from different layers. Nevertheless, our work

establishes a clear baseline for UAV re-identiﬁcation

performance, of which the beneﬁts are evident within

potential UAV tracking frameworks.

ACKNOWLEDGEMENTS

This work is funded in part by the Future Avia-

tion Security Solutions (FASS) programme, a joint

VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications

144

initiative between DfT and the Home Ofﬁce (Ref:

007CD). This work is made possible by the WOS-

DETC dataset.

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