Unidentiﬁed Floating Object Detection in Maritime Environment

Darshan Venkatrayappa

, Agn

es Desolneux

, Jean-Michel Hubert

and Josselin Manceau

Centre Borelli, ENS Paris-Saclay, Gif-sur-Yvette, France

iXblue, Saint-Germain-en-Laye, France

Keywords:

Floating Object Detection, Unsupervised Learning, Self-similarity.

Abstract:

In this article, we present a new unsupervised approach to detect unidentiﬁed ﬂoating objects in the mar-

itime environment. The proposed approach is capable of detecting ﬂoating objects online without any prior

knowledge of their visual appearance, shape or location. Given an image from a video stream, we extract the

self-similar and dissimilar components of the image using a visual dictionary. The dissimilar component con-

sists of noise and structures (objects). The structures (objects) are then extracted using an a contrario model.

We demonstrate the capabilities of our algorithm by testing it on videos exhibiting varying maritime scenarios.

1 INTRODUCTION

In the maritime environment, one can encounter two

categories of ﬂoating objects. The ﬁrst category in-

volves military and commercial ships, boats, trolleys,

small buoys etc. The second category called Uniden-

tiﬁed Floating Objects (UFOs) include objects like

drifting containers, drifting iceberg, drifting cargo

boxes, driftwood, debris etc. These UFOs are ran-

dom, diverse, rare and are a threat to maritime trans-

portation. To limit the risk in the maritime domain,

there is a need to detect and track the ﬂoating ob-

jects and particularly the UFOs. From a century ago

until recently, ranging devices such as Lidar, radar

(Onunka and Bright, 2010) and sonar (Heidarsson

and Sukhatme, 2011) have been used to counter the

above-mentioned risky scenarios. But, Lidar is ex-

pensive and radar data is sensitive to the variation in

the climate, the shape, size, and material of the tar-

gets. As a result, the ranging devices have to be sup-

plemented by other sensors such as cameras for de-

tecting UFOs.

To tackle the limitations of ranging devices, the

computer vision community has resorted to camera-

based object detection and tracking. Several re-

searchers (Bloisi et al., 2014), (Heidarsson and

Sukhatme, 2011), (Prasad et al., 2017) have used

camera or a combination of camera and ranging de-

vice for ﬂoating object detection. We can ﬁnd a

detailed description about the challenges and differ-

ent sensors used in the maritime scenario in (Prasad

et al., 2017). Most of the state-of-the-art Back-

ground Subtraction (BS) algorithms (St-Charles and

Bilodeau, 2014), (St-Charles et al., 2014), (Oliver

et al., 2000),(Elgammal et al., 2000), (Sobral and Va-

cavant, 2014) etc. that address dynamic backgrounds

have been used in the maritime domain. A review

of different background subtraction algorithms used

in maritime object detection can be found in (Prasad

et al., 2019).

The authors of (Socek et al., 2005) propose

a Bayesian decision framework based hybrid fore-

ground object detection algorithm in the maritime do-

main. Kristan et al. (Kristan et al., 2015) use Gaus-

sian Mixture Model (GMM) to segment water, land

and sky regions. The GMM relies on the availabil-

ity of the precomputed priors using training data. The

major drawback of this method is the expensive pre-

training step. The Authors of (Bloisi and Iocchi,

2012) propose a non-parametric Background Subtrac-

tion method for the maritime scenario using a 3 step

approach consisting of online clustering, background

update and noise removal. The problem with these

methods is that most of them fail with varying back-

ground or lighting conditions and the ones that suc-

ceed are computationally very expensive. The au-

thors of (Sobral et al., ) and (Karnowski et al., 2015)

use Robust Principal Components Analysis (RPCA)

to detect and track sailboats and dolphins respectively.

Most of the methods based on low rank and sparse

representation are not suitable for real-time applica-

tions as they are highly complex and require a col-

lection of frames to detect the object. With the ad-

vent of deep learning, many new supervised mod-

els (Moosbauer et al., 2019), (Bovcon and Kristan,

2020), (Yang et al., 2019), (Lee et al., 2018) have been

proposed for ﬂoating object detection in general and

ship detection in particular. An evaluation of different

Venkatrayappa, D., Desolneux, A., Hubert, J. and Manceau, J.

Unidentiﬁed Floating Object Detection in Maritime Environment.

DOI: 10.5220/0010771800003124

In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP, pages

65-74

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

Figure 1: Workﬂow.

deep semantic segmentation networks for object de-

tection in maritime surveillance can be found in (Cane

and Ferryman, 2018). Most of the supervised and un-

supervised deep learning methods for object detection

require a large amount of training data and in our case

data is sparse as we have no prior knowledge about the

UFO encountered in the ocean. One of the advantage

of our method is it considers the current frame as the

training data.

In the far sea scenario, the background (sea and

sky) have their respective color and texture. Float-

ing objects can be detected by separating the back-

ground from the object. Our approach can be con-

sidered as zero-shot internal learning (Shocher et al.,

2018) without a neural network. We detect the mar-

itime objects by removing the self-similar component

from the image. The self-similar component(self-

similar image) is obtained using a visual dictionary.

The dissimilar component(dissimilar image) is then

the difference between the original image and the self-

similar component. Thus, the self-similar component

of the image is removed and the dissimilar image is

left with the noise and structures (objects). The noise

and the structures can be separated by statistical tests

based on an a contrario approach (Desolneux et al.,

2008). Our algorithm works on the dissimilar com-

ponent at different scales. This work mainly concen-

trates on detecting ﬂoating objects in the far sea using

a camera mounted on a ship. That being said, our al-

gorithm performs well near the shore with a camera

onshore and is able to detect big, small, far and near

objects in the sea at different climatic conditions. Our

method can also be used to detect trash ﬂoating on

rivers and canals. It is to be noted that here, we are

not concerned with labeling or classifying the object

as boat, ship, cargo container, etc. instead, we assume

that any ﬂoating object and in particular the UFOs are

obstacles and needs to be detected. Our work is more

of an early warning system.

2 WORKFLOW

The workﬂow of the proposed algorithm is shown in

Fig. 1. Let y be an original image obtained during the

image acquisition process (Fig. 2a). We are interested

in a self-similar image ˆx (Fig. 2b), where each of its

patches admits a sparse representation in terms of a

learned dictionary (Elad and Aharon, 2007). For each

pixel position (i, j) of the image y, we denote by R

i j

the size n column vector formed by the gray-scale lev-

els of the squared

√

n ×

√

n patch of the image y and

the top-left corner of the patch is represented by the

coordinates (i, j). The goal is to learn a dictionary

(Fig. 2e) of size n ×k, with k ≥ n and whose columns

are normalized. Here, an initialization of the dictio-

nary denoted by D

init

(Fig. 2d) is required which is

done using random patches from the original image

y. We learn

D using the K-SVD algorithm (Lebrun

and Leclaire, 2012) and use the learnt

D to obtain the

self-similar image.

In the ﬁrst step, we use the ﬁxed dictionary

D to

compute the sparse approximation

α of all the patches

i j

y of the image in

D. i.e. for each patch R

i j

y a col-

umn vector

i j

of size k is built such that it has only a

few non-zero coefﬁcients and such that the distance

between R

i j

y and its sparse approximation

i, j

very small.

Argmin

i j

such that kR

i j

y −

i j

≤ ε

(1)

Where k

i j

refers to the number of non-zero co-

efﬁcients of

i j

also known as l

norm of

i j

. This

is a NP hard problem, and we make use of Orthog-

onal Recursive Matching Pursuit (ORMP) to get an

approximate solution. In Eq. (1), ε is used during the

break condition of the ORMP,

D is of size n ×k,

i j

is a column vector of size k ×1 and R

i j

y is a column

vector of size n ×1. In the second step, we update

the columns of the dictionary

D one by one, to reduce

the quantity in Eq. (2) without increasing the spar-

sity penalty

i j

such that all the patches in the image

y are efﬁcient. This is achieved using the K-SVD al-

gorithm. More details about K-SVD can be found in

(Lebrun and Leclaire, 2012).

∑

i, j

i j

−R

i j

(2)

We repeat the above two steps for some iterations

say K iter. Once these K iter iterations are done,

each patch R

i j

y of the image y corresponds to the self-

similar version

i j

. In the third and ﬁnal step, we

reconstruct the complete self-similar image from all

the self-similar patches by solving the minimization

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

(a) (b) (c) (d) (e)

Figure 2: (a). Original image y, (b). self-similar component ˆx, (c). dissimilar component r (contrast and brightness adjusted),

(d). Random patches from y used to learn the dictionary, (e). Final learnt dictionary

D. These image are obtained by using

the dictionary of size 512 and patch size 64 (8x8).

problem in Eq. (3). First term in Eq. (3) represents a

ﬁdelity term which controls the global proximity to

our reconstruction ˆx with the input image y . The

second term controls the proximity of the patch R

i j

ˆx

of our reconstruction to Dα

i j

(Lebrun and Leclaire,

2012).

ˆx = Argmin

x∈IR

λkx −yk

∑

i, j

i j

−R

i j

(3)

We extend the above algorithm to colour images by

concatenating the R,G,B values of the patch to a sin-

gle column. Thus the algorithm learns the correlation

between the color channels resulting in a better update

of the dictionary. The dissimilar component r (Fig.

2c) is extracted by taking the pixel-wise difference be-

tween y and ˆx as in r(i, j,ch) = y(i, j,ch)− ˆx(i, j, ch).

Where, i, j and ch represents the pixel coordinates and

the channel number respectively. Thus obtained dis-

similar image contains only noise and structures (ob-

jects) as it is free from self-similar component. The

intuition is that it is straightforward to detect salient

regions/objects in the dissimilar image compared to

detection in the original image y. We use a multi-scale

approach to detect the structures of different sizes.

We follow (Lowe, 2004) to obtain images at differ-

ent scales and learn individual dictionary for each of

the scaled version of the original image. Finally, we

construct the dissimilar image at different scales, as

previously explained.

2.1 Object Detection and Localization

The a contrario detection theory was primarily pro-

posed by (Desolneux et al., 2008) and has been suc-

cessfully employed in many computer vision applica-

tions such as shape matching (Mus

e et al., 2003), van-

ishing point detection (Lezama et al., 2017), anomaly

detection (Davy et al., 2018), spot detection (Gros-

jean and Moisan, 2009) etc. The a contrario frame-

work is based on the probabilistic formalization of

the Helmholtz perceptual grouping principle. Ac-

cording to this principle, perceptually meaningful

structures represent large deviations from random-

ness/naive model. Here, the structures to be detected

are the co-occurrence of several local observations

(Desolneux et al., 2008).

We deﬁne a naive model by assuming that all lo-

cal observations are independent. By using this a

contrario assumption, we can compute the probability

that a given structure occurs. More precisely, we call

the number of false alarms (NFA) of a structure con-

ﬁguration, its expected number of occurrences in the

naive model. We say that a structure is ε-meaningful

if its NFA is smaller than ε. The smaller the ε, the

more meaningful the event. Given a set of random

variables (U

)

i∈[|1,N|]

with observed values (u

)

, we

deﬁne the NFA of each observation as NFA(u,i) :=

NP(U

≥u

),(Eq.(4)) where P is the a contrario prob-

ability distribution (white noise in general). Here N is

the total number of tests, which is nothing but the total

number of pixels in all the images at different chan-

nels and scales. We will apply this NFA to u being

the dissimilar component, and the naive model is con-

structed in such a way that each pixel of the dissimilar

image follows a standard normal distribution.

We aim to detect structures in the dissimilar image

r. The dissimilar image r is unstructured, similar to a

coloured noise and not necessarily Gaussian. A care-

ful study of the distribution of the dissimilar image

shows that it follows a generalized Gaussian distribu-

tion (Davy et al., 2018). A non-linear transform is

used to re-scale the dissimilar image to ﬁt a centred

Gaussian distribution with unit variance. This cen-

tered Gaussian distribution with unit variance is con-

sidered as the naive model. The naive model doesn’t

require the noise to be uncorrelated. Since structures

are expected to deviate from this naive model, this

amounts to checking the tails of the Gaussian and to

retain high values as signiﬁcant if their tail has a very

small area. Similar to (Grosjean and Moisan, 2009),

we convolve the dissimilar image with a kernel K

given radius, which results in a new image r = r ∗K

Thus obtained r is normalized to have a unit vari-

ance. As we have assumed the dissimilar component

Unidentiﬁed Floating Object Detection in Maritime Environment

Table 1: Dataset and its properties.

Seq Name Number of frames Resolution Time taken(sec) Details Camera

S.1 Ship-wreckage 257 1276 x 546 10 Wreckage from a broken ship On-board, camera motion

S.2 Floating-Container 300 640 x 352 3 Containers ﬂoating in sea On-board, camera motion

S.3 Sinking-Trolley 381 1920 x 1080 28 Debris of varying size On-board, camera motion

S.4 Space-capsule 257 1276 x 438 9 Space capsule being retrieved back On-board, no camera motion

S.5 MVI 1644 VIS 252 1920 x 1080 28 From SMD (Prasad et al., 2017), contains big ships On-shore, no motion

S.6 Rainy 250 1920 x 1080 28 Rainy and windy condition camera motion

S.7 MVI 0788 VIS 299 1920 x 1080 28 From SMD (Prasad et al., 2017), Far and small objects Sever camera motion

to be a stationary Gaussian ﬁeld, the result after ﬁl-

tering is also Gaussian. We use N

= 3 number of

disks with radius 1,2 and 3 at each scale to detect the

salient structures in the dissimilar image. We detect

the structures on both the tail of the distribution using

the NFA given in Eq.(4). The number of tests in our

case is given by N = N

×N

∑

scale

−1

|Ω

|. Here,

refers to the number of disk kernels, N

refers to

the number of channels, N

scale

is the number of scales

used and Ω is the set of pixels in the dissimilar image

at a given scale.

By using the above approach, structures are de-

tected at a certain radius of the kernel. Using the

center and the radius of detection, we construct a

square bounding box. Many of these bounding boxes

overlap. We use the opencv function such as ”ﬁnd-

Contours” and ”approxPolyDP” to fuse the overlap-

ping bounding boxes and get a single big rectangular

bounding box. Thus obtained bounding boxes have

many false detection due to spurious dynamics of wa-

ter. One of the easiest ways to reﬁne false detection is

to ignore the bounding box which has no key-points

present in it. A combination of key-points from SIFT

(Lowe, 2004) and SURF (Bay et al., 2008) detectors

are used as they provide a good coverage of the im-

age space, including corners, edges and textured ar-

eas. In our case, we make use of 200 most dominant

key-points from each of the detectors to reﬁne false

detection. Another approach to reﬁne false detection

is to track all the detected pixels for a few frames. De-

tections on the water(false detections) loose the track

where as majority of the detections on the object are

tracked correctly. The tracks fail in case of camera

motion or motion of the boat as in video sequence 7.

3 EXPERIMENTS AND RESULTS

In the dictionary learning part, we set the patch size n

to 16 and the size of the dictionary k is ﬁxed to 128.

The break condition for ORMP ε is set to 10

−6

. The

number of iterations in the K-SVD process K iter is

ﬁxed to 7. λ in Eq(3) is set to 0.15. These parameters

are choosen emperically as they give a good trade-off

between the size of the detected object and the speed

of the algorithm. More details about these parameters

can be found in (Lebrun and Leclaire, 2012). In the

a contrario detection part, we have experimented with

both ε=10

(or logε = 2) and ε = 10

−2

(or logε= −2)

where logε is the logarithm of ε. N

scales

represents the

number of scales used in the multi-scale approach and

set to 4. The Radius of the circular kernel K

= 1,2,3.

All the parameters are empirically chosen. In the mar-

itime object detection scenario, only a few data-sets

have been proposed and most of these data-sets are

intended for ship detection. As the UFOs are random

and sporadic, it is very difﬁcult to come up with a

database/data-set and there is hardly any dataset avail-

able. Here, we introduce a small data-set by extract-

ing portion of videos from YouTube. We manually

annotate the dataset by drawing ground truth bound-

ing boxes around the object. To demonstrate the ca

pabilities of our algorithm to detect ships/boats in the

sea, we make use of the Singapore maritime dataset

(Prasad et al., 2017). The features of our data-set are

tabulated in Table 1.

The algorithm was coded in C++ using OpenCV

library and tested on a laptop with 8 cores. The av-

erage time taken (in seconds) for each frame of the

sequence is given in the 5th column of Table 1. In our

experiments, the dictionary is learnt for every frame.

Real time performance can be achieved by initially

using a pre-learnt dictionary and then learning the dic-

tionary (online and in parallel) for every M duration

of time. Most of the modern CNN based object de-

tection methods outperform our method as they are

completely supervised in nature. Our approach is un-

supervised. So, we compare our method with unsu-

pervised traditional methods. As our method sepa-

rates the self-similar and the dissimilar content to de-

tect the object it can be considered as a background

subtraction method.

In the maritime object detection literature some

authors have used saliency methods for comparison.

So, The results of our algorithm are compared with

i) Two Background Subtraction (BS) methods: SuB-

SENSE and LOBSTER. ii) Two saliency detection

methods: spectral Residual Approach (SRA) (Hou

and Zhang, 2007) and ITTI (Itti et al., 1998). The

code for SRA and ITTI can be found in (Hou and

Zhang, 2007) and (Walther and Koch, 2006). The

code for SuBSENSE (St-Charles et al., 2014) and

LOBSTER (St-Charles and Bilodeau, 2014) can be

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

Table 2: Quantitative Evaluation : DR: Detection Rate, FAR: False Alarm Rate.

logε=-2 logε=2 IITI SRA LOBSTER SuBSENSE

Seq DR FAR DR FAR DR FAR DR FAR DR FAR DR FAR

1 0.701 0.080 0.853 0.124 0.434 0.493 0.707 0.383 0.53 0.467 0.494 0.754

2 0.813 0.026 0.934 0.010 0.683 0.023 0.682 0.201 0.8353 0.067 0.743 0.092

3 0.552 0,142 0.688 0,371 0.118 0.274 0.501 0.487 0.446 0.875 0.622 0.792

4 0.891 0.167 0.921 0.199 0.379 0.601 0.876 0.402 0.863 0.523 0.190 0.080

5 0.531 0.074 0.512 0.193 0.621 0.234 0.689 0.523 0.23 0.79 < 0.0001 0.998

6 0.793 0.312 0.988 0.507 0.972 0.401 0.754 .748 0.17 0.977 0.536 0.949

7 0,653 0,464 0,874 0,542 0,514 0,724 0,538 0,741 0,584 0,928 0,354 0,836

found in (Sobral, 2013). For all the four methods, we

have used the default parameters provided by the au-

thors. Fig. 3, Fig. 4 and Fig. 5 presents the qualitative

comparison of the methods for some of the videos.

The red and green bounding boxes present in the 2nd

row of each layer in Fig. 3 represent the detections ob-

tained by our algorithm with logε = 2 and logε = −2

respectively. The bounding boxes in orange and pink

in the 3rd row of each layer in Fig. 3 belong to ITTI

and SRA, respectively. LOBSTER and SuBSENSE

methods are shown in 4th and 5th rows respectively.

False detections from our approach are shown in Fig.

6. The detections with logε = 2 takes into account

many weak detections, meaning it detects many pix-

els as part of the object and this results in a red bound-

ing box which is sometimes much bigger than the ob-

ject itself. The detection with logε = −2 takes into

account only the robust detections, meaning a small

number of pixels are detected as part of an object and

this results in a green bounding box which is usually

smaller than the object. An object can contain many

of these small green bounding boxes. Thus by varying

the logε, we can control the number of false alarms.

Lower the logε, the stronger and accurate the detec-

tions are. Both SRA and ITTI result in many false

detections. In our experiments, varying the patch size

and dictionary size increases the algorithm run-time

with minor improvements in the detection results.

Quantitative evaluation is performed by calculat-

ing the True Positive TP (If a bounding box is present

on the Ground truth object), False Positive FP (If we

detect an object when there is none) and False Neg-

ative FN (If we fail to detect the Ground truth ob-

ject). Using these three measures, we further calcu-

late the Detection Rate (DR = TP/(TP + FN)) and

False Alarm Rate (FAR = FP/(TP + FP)). Ideally,

DR should be high, whereas the FAR should be as

low as possible.

Most of the supervised object detection ap-

proaches use Intersection over Union (IOU) as a met-

ric to evaluate performance. As our approach is un-

supervised, we don’t get a complete detection of the

object. Some objects are completely detected and oth-

ers may have multiple BB’s detecting different parts

of the same object. Additionally, our approach us-

ing logNFA = −2 prioritises the most salient parts

of the object. So, the IOU metric is not suitable for

our application. Similar to (Shin et al., 2015), for ev-

ery frame in a sequence, we check for the presence

of detected Bounding Box(BB) on the ground truth

(GT) object. If the detected BB has more than 30%

overlap with the ground truth we consider it as a True

Positive(TP). In some cases multiple BB’s may be de-

tected on a single GT object. In such a scenario, we

fuse the areas of the BB’s and if the fused area is more

than 30% of the ground truth BB then we consider it

as TP. A FP is detected if a BB is present in the back-

ground (sea, sky or land) and a FN is detected if the

Ground truth object doesn’t contain any detected BB.

Thus, for a given video sequence the ﬁnal value of FP,

FN and TP is obtained by accumulating scores across

all the frames of the sequence.

The quantitative results of our experiments are

tabulated in Table 2. From this table, it is evident that

our algorithm with logε = −2 gives minimum FAR

for 5 of the video sequences. As logε = −2 takes

into account only the robust detections, we can ex-

pect the FAR to be minimum, thus reducing the false

alarms. This is also a reason for our algorithm with

logε = −2 to have more FN compared to logε = 2. For

most of the video sequence, maximum DR is achieved

by our algorithm with logε = 2, as it takes into ac-

count both strong and weak detections. Thus, our al-

gorithms outperforms the other algorithm for most of

the video sequence. We have compared our algorithm

with many other BS methods provided by (Sobral,

2013). But, here in this paper we show the results

for 2 prominent BS methods. Most of the BS meth-

ods fail due to camera motion, which is often the case

in onboard maritime scenario. LOBSTER method ex-

hibits lower FAR compared to SuBSENSE. The two

saliency methods perform weakly in detecting small

objects but perform better than the two BS methods.

Unidentiﬁed Floating Object Detection in Maritime Environment

S.1(39) S.1(97) S.1(149) S.2(6) S.2(50) S.2(105)

S.5(8) S.5(19) S.5(38) S.4(3) S.4(67) S.4(158)

Figure 3: Qualitative results : The sequence number and frame numbers are indicated below each of the image. Top row of

each layer represents the dissimilar image (contrast and brightness adjusted). 2nd row of each layer indicates the detection

made by our algorithm with logε=2 (Red bounding boxes) and logε=−2 (Green bounding boxes). Detections in the 3rd row

of each layer belongs to ITTI (Orange bounding boxes) and SRA (Pink bounding boxes). 4th and 5th row shows the results

from LOBSTER and SuBSENSE respectively.

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

S.3(1) S.3(88) S.3(273) S.3(358)

S.6(41) S.6(104) S.6(195) S.6(246)

Figure 4: Qualitative results : The sequence number and frame numbers are indicated below each of the image. Top row of

each layer represents the dissimilar image (contrast and brightness adjusted). 2nd row of each layer indicates the detection

made by our algorithm with logε=2 (Red bounding boxes) and logε=−2 (Green bounding boxes). Detections in the 3rd row

of each layer belongs to ITTI (Orange bounding boxes) and SRA (Pink bounding boxes). 4th and 5th row shows the results

from LOBSTER and SuBSENSE respectively.

Unidentiﬁed Floating Object Detection in Maritime Environment

(a) S.7(47) (b) S.7(67) (c) S.7(166) (d) S.7(231)

Figure 5: Qualitative results : The sequence number and frame numbers are indicated below each of the image. First row

indicates the detection made by our algorithm with logε=2 (Red bounding boxes) and logε=−2 (Green bounding boxes).

Detections in the 2nd row of each layer belongs to ITTI (Orange bounding boxes) and SRA (Pink bounding boxes). 3rd and

4th row shows the results from LOBSTER and SuBSENSE respectively.

(a) S.1(52) (b) S.1(110) (c) S.2(132) (d) S.2(270)

(e) S.4(64) (f) S.4(129) (g) S.5(127) (h) S.5(157)

Figure 6: Qualitative results: False detections resulting from our approach. Sequence number and frame number are below

each image.

4 CONCLUSION AND FUTURE

WORK

We have proposed an unsupervised ﬂoating object de-

tection algorithm speciﬁc to the maritime environ-

ment. The effectiveness of our approach was demon-

strated on challenging video sequences exhibiting

varying challenges of far sea maritime scenarios,

moving camera and small targets. The proposed algo-

rithm exhibits good performance in detecting uniden-

tiﬁed ﬂoating objects of varying size and shape. How-

ever, the algorithm has limited ability in the pres-

ence of strong sun glint. Future work will focus on

the temporal aspect, tracking of detected objects and

real-time (GPU) implementation of the proposed al-

gorithm.

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

ACKNOWLEDGEMENTS

The authors would like to thank Axel Davy and

Thibaud Ehret for providing the implementation of a

contrario approach.

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