Fast Target Redetection for CAMSHIFT using Back-projection and

Histogram Matching

Abdul Basit

1,2

, Matthew N. Dailey

, Pudit Laksanacharoen

and Jednipat Moonrinta

Department of Computer Science and Information Management, Asian Institute of Technology,

Klong Luang (12120), Pathumthani, Thailand

Department of Computer Science and Information Technology, University of Balochistan, Quetta, Pakistan

Mechanical Engineering, King Mongkut’s University of Technology (North Bangkok) Bangsue, Bangkok, Thailand

Keywords:

Monocular Visual Tracking, Redetection, Adaptive Histogram, CAMSHIFT Tracker, Backprojection.

Abstract:

Most visual tracking algorithms lose track of the target object (start tracking a different object or part of

the background) or report an error when the object being tracked leaves the scene or becomes occluded in

a cluttered environment. We propose a fast algorithm for mobile robots tracking humans or other objects

in real-life scenarios to avoid these problems. The proposed method uses an adaptive histogram threshold

matching algorithm to suspend the CAMSHIFT tracker when the target is insufﬁciently clear. While tracking

is suspended, any method would need to continually scan the entire image in an attempt to redetect and

reinitialize tracking of the speciﬁed object. However, searching the entire image for an arbitrary target object

requires an extremely efﬁcient algorithm to be feasible in real time. Our method, rather than a detailed search

over the entire image, makes efﬁcient use of the backprojection of the target object’s appearance model to

hypothesize and test just a few candidate locations for the target in each image. Once the target object is

redetected and sufﬁciently clear in a new image, the method reinitializes tracking. In a series of experiments

with four real-world videos, we ﬁnd that the method is successful at suspending and reinitializing CAMSHIFT

tracking when the target leaves and reenters the scene, with successful reinitialization and very low false

positive rates.

1 INTRODUCTION

In security and surveillance, one useful type of au-

tonomous vehicle is the pursuit robot, a robot able to

autonomously pursue a target object. Our research

focuses on the use of surveillance robots such as the

iRobot PackBot for autonomous target pursuit. Target

pursuit robots are useful in any situation where a per-

son or object must be tracked but human access would

be impossible or life threatening, e.g., tracking a vic-

tim in a building on ﬁre, tracking a terrorist during

an ongoing attack, or tracking a suspicious individual

who has entered a secure area. An example pursuit

robot from our lab is shown in Fig. 1.

Cameras are becoming increasingly inexpensive

and useful sensors for autonomous vehicles. Vision-

guided robots must use their cameras to avoid obsta-

cles and plan optimal paths in order to accomplish

their tasks. In addition to obstacle avoidance and path

planning, vision-guided pursuit robots must addition-

ally perform visual target tracking using their cam-

Figure 1: All-terrain robot for tracking and pursuit of arbi-

trary objects using a monocular camera.

eras. We are particularly interested in the viability of

monocular vision as the main sensor for autonomous

pursuit.

Tracking an object during target pursuit requires

a tracker that is both sufﬁciently accurate and sufﬁ-

ciently fast to keep track of the object in real time.

We categorize the common tracking algorithms as to

whether they utilize feature matching, optical ﬂow,

507

Basit A., N. Dailey M., Laksanacharoen P. and Moonrinta J..

Fast Target Redetection for CAMSHIFT using Back-projection and Histogram Matching.

DOI: 10.5220/0004670605070514

In Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISAPP-2014), pages 507-514

ISBN: 978-989-758-009-3

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

or feature histograms. Feature matching algorithms

such as SIFT (Zhou et al., 2009), SURF (Ta et al.,

2009), and shape matching algorithms such as con-

tour matching (Yokoyama and Poggio, 2005) are too

computationally expensive to be considered for real-

time tracking by a moving robot with modest com-

pute resources. Optical ﬂow methods (Denman et al.,

2007) may within reach in terms of speed, but they do

not maintain an appearance model. This means they

are unable (by themselves) to recover from occlusions

and objects leaving the ﬁeld of view.

Histogram-based trackers, on the other hand, are

not only fast, but also maintain an appearance model

that is potentially useful for recovering tracking af-

ter an occlusion or reappearance in the ﬁeld of view.

When the target object is occluded or leaves the ﬁeld

of view (or when the tracker gets lost for some rea-

son), we simply need to suspend tracking, continually

search the image for the reappearance of the target ob-

ject, then, once the object has reappeared in the scene,

reinitialize the tracker.

In this paper, we thus consider the problem of re-

detecting the target object once a feature histogram-

based tracking method has been suspended. We as-

sume that the goal is to search for the target object in

every frame without any bias as to where the object

might appear.

The common approach to object search in com-

puter vision is the sliding window. The typical

algorithm slides a detection window over the im-

age at multiple scales, and at each step, the se-

lected image window’s feature histogram is com-

pared with the stored color histogram (the appear-

ance model). The naive sliding window approach is

computationally inefﬁcient, however, and many re-

searchers have developed methods to improve the ef-

ﬁciency of sliding window calculations in different

contexts. Porikli (Porikli, 2005) propose an “integral

histogram” method using integral images. The inte-

gral image is a well-known technique that supports

calculating the sum of the values in a rectangular re-

gion of a feature plane in constant time. Perreault

and Hebert (Perreault and Hebert, 2007) compute his-

tograms for median ﬁltering efﬁciently by maintain-

ing separate columnwise histograms, and, as the slid-

ing window moves right, ﬁrst updating the relevant

column histogram then adding and subtracting the rel-

evant column histograms to the histogram for the slid-

ing window. Sizintsev et al. (Sizintsev et al., 2008)

take a similar approach to obtain histograms over slid-

ing windows by efﬁciently updating the histogram us-

ing previously calculated histograms for overlapping

windows.

However, although this work demonstrates that it

is possible to compute sliding window histograms in

constant time per window location, it may still not be

fast enough if multiple window sizes and aspect ratios

must be considered, and furthermore, ﬁnding a sin-

gle best rectangular window still does not give a pre-

cise object shape and orientation. Chen et al. (Chen

et al., 2008) address the speed issue by scattering ran-

domly generated elliptical regions over the image in

a ﬁrst rough detection phase and address the preci-

sion issue by performing ﬁne searches from the more

likely candidate regions. In this paper, we propose a

backprojection-based method for the rough detection

phase that does not require breaking the image into

regions.

CAMSHIFT (Continuously Adaptive Mean Shift)

(Bradski, Oct; Allen et al., 2004) is a fast and ro-

bust feature histogram tracking algorithm potentially

useful for mobile robots in outdoor environments.

The method begins with manual initialization from

a target image patch. It then tracks the region using

a combination of color histograms, the basic mean-

shift algorithm (Comaniciu et al., 2000; Comaniciu

et al., 2003), and an adaptive region-sizing step. It is

scale and orientation invariant. The main drawback

of CAMSHIFT is that if the target leaves the ﬁeld of

view or is occluded, the algorithm either reports an

error or starts tracking a completely different object.

This limitation of CAMSHIFT lies in the fact that

on each frame, it performs a global backprojection

of the appearance model followed by a local search

for the best target region beginning from the previous

frame’s estimated region. Since the method performs

a search for a local peak in the global backprojection,

it is easily distracted by background objects with sim-

ilar color distributions.

We propose in our previous paper (Basit et al.,

2012), a motion model based on EKF to improve

the pursuit robot trajectory relative to the target path.

We fused pursuit robot (differential drive) kinematics

and target dynamics with a model of the color region

tracking sensor using an extended Kalman ﬁlter.

In this paper, we extend our work to pro-

pose an efﬁcient method to 1) intelligently suspend

CAMSHIFT tracking when the target leaves the scene

or is occluded and 2) reinitialize the tracker when the

target object returns to view. The decision to sus-

pend tracking is based on an adaptive threshold ap-

plied to the dissimilarity between the CAMSHIFT

region’s color histogram and the stored appearance

model, as well as heuristic limitations on changes

in the tracking window’s size. The reinitialization

method is based on backprojection of the appearance

model, thresholding of the per-pixel likelihood, and

connected components analysis, resulting in a collec-

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508

tion of candidate regions, the best of which is selected

if it is sufﬁciently likely according to the appearance

model.

The idea of using the global backprojection of a

color histogram to quickly obtain candidate detection

regions is related to work by Chen et al. (Chen et al.,

2009), who, in the context of a pure object detec-

tion task, not involving tracking, propose a rough de-

tection stage using backprojection and morphological

analysis to provide an initial guess for a mean shift

style precise detection stage. Our method is similar

in the use of the global backprojection. However, we

only perform global detection while tracking is sus-

pended due to occlusion or an object that has left

the ﬁeld of view. The methods for multiple candi-

date region hypothesis checking and adaptive similar-

ity thresholding based on observation of the object’s

appearance over time during tracking are also new

contributions.

In an empirical evaluation on real-world videos,

we show that the proposed method is a robust method

for target tracking and reinitialization during pursuit

that is successful at reinitialization, has very low false

positive rates, and runs in real time.

2 TRACKING AND TARGET

REDETECTION

In this section, we describe our algorithms for sus-

pending CAMSHIFT tracking, redetecting the target,

and reinitializing tracking. The overall ﬂow of the al-

gorithm is shown in Fig. 2. The following sections

provide a brief overview of CAMSHIFT and the de-

tails of our methods.

2.1 CAMSHIFT Object Tracking

Although our methods are compatible with any fast

feature histogram-based image region tracker, in our

experiments, we use CAMSHIFT (Bradski, Oct).

CAMSHIFT applies traditional mean shift to the

backprojection of the appearance model (color his-

togram) and adds an adaptive region sizing step.

Given an initial detection window in frame t − 1 and

the backprojection of the appearance model onto the

image acquired at time t, the method computes, over

the detection window, the center of mass of the back-

projection, shifts the detection window to the com-

puted center of mass, and repeats the process until

convergence. The method additionally calculates the

zeroth, ﬁrst, and second order moments of the back-

projection in the ﬁnal detection window at (x

) and

Figure 2: Flow of proposed target tracking and redetec-

tion method. The tracking phase (green block) simply uses

CAMSHIFT, and maintains an adaptive histogram distance

threshold. When the target object leaves the scene or is

occluded, we switch to the detection phase (blue block),

which proceeds as follows. 1) We backproject the appear-

ance model to determine the consistency of each pixel with

the appearance model. 2) We binarize the backprojection to

eliminate weakly consistent image regions. 3) We generate

the connected components using the morphological ﬁlters.

4) We generate a set of candidate regions by eliminating

inconsistent connected components. 5) We ﬁnd the most

consistent region using histogram comparison between can-

didate region and target histogram.

updates the aspect ratio r and size (w, h) of the de-

tection window according to the calculated moments

(formulae are from (Exner et al., 2010)):

, y

, r =

w =

· r, h =

/r.

FastTargetRedetectionforCAMSHIFTusingBack-projectionandHistogramMatching

509

2.2 Suspending Tracking

CAMSHIFT works extremely well so long as the

target appearance remains consistent and distinctive

with respect to the background. However, when the

target object leaves the scene, is occluded, or im-

pinges a background region with a similar color distri-

bution, the tracking region tends to change rapidly in

size, growing into background regions or moving to a

different location completely. When this happens dur-

ing a target pursuit application, lest the pursuit motion

planner become confused, it is important to suspend

tracking and attempt to redetect the target object.

To achieve this, as a ﬁrst measure we impose sim-

ple constraints on the target detection window’s lo-

cation and size. If the target object’s estimated size

or location changes by an amount inconsistent with

robot and target dynamics, clearly, the tracker is lost

and needs to be reinitialized.

However, such simple location and size con-

straints are not sufﬁcient. We ﬁnd that in cluttered

scenes, when the target is partially or wholly occluded

or leaves the scene, CAMSHIFT tends to get dis-

tracted by background regions, oftentimes without a

sufﬁciently large change in position or size to ﬂag sus-

pension.

We therefore, before committing to CAMSHIFT’s

estimate of the target at time t, verify the quality of the

candidate detection region using an adaptive thresh-

old applied to the dissimilarity between the candi-

date region’s color histogram H

and the appearance

model H

. We use the default OpenCV histogram

comparison function (Bradski, ), which returns a dis-

tance based on the Bhattacharyya coefﬁcient

≡ d(H

) =

1 −

∑

(i) · H

(i). (1)

(The implementation also normalizes the histograms

to sum to 1.) The resulting distance varies between

0, for identical histograms, to 1, for non-overlapping

histograms.

The histogram comparison threshold is computed

adaptively. We keep running estimates of the distance

measure’s mean and standard deviation

= µ

t−1

− µ

t−1

(t − 2)σ

t−1

+ (d

− µ

t−1

)(d

− µ

)

t − 1

and then suspend tracking when we obtain a new dis-

tance that deviates too far from the running mean, i.e.,

when

> µ

t−1

+ θσ

t−1

θ is a threshold on the z-score of the newly measured

distance. We use θ = 3 in our experiments.

(a) (b)

(e) (f)

Figure 3: Example target redetection steps. (a) Example

image I

containing the target. (b) Backprojection BP

appearance model. (c) Binarized and ﬁltered version of BP

(d) Bounding boxes of candidate regions after ﬁltering out

small connected components. (e) Candidate regions from

(d) overlaid on I

. (f) Reinitialized CAMSHIFT result.

2.3 Target Redetection

While tracking is suspended, on every frame, we need

to execute the target redetection algorithm. The ﬂow

is shown in the light blue box in Fig. 2, and an exam-

ple of the result of each step is shown in Fig. 3. We

detail each step here.

2.3.1 Backproject the Appearance Model

The color histogram H

gives us the probability

P(I

(x,y) | target) of observing a pixel with color

(x,y) given that the pixel is actually in the target

region. Backprojection of the appearance model H

simply means that we generate a new image BP

such

that BP

(x,y) = P(I

(x,y) | target) according to H

will have in general several clusters of pixels with

high values, indicating some degree of consistency of

the region with H

. An example is shown in Fig. 3(b).

2.3.2 Binarize Likelihood Image

In this step, to eliminate weak matches between I

and the appearance model, we threshold BP

using the

standard Otsu method to obtain a binary image C

in-

dicating candidate target pixels.

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2.3.3 Generate Connected Components

In this step, we apply morphological erosion and dila-

tion to C

to eliminate noise and ﬁll gaps, then extract

the connected components. In our experiments, we

use a square structuring element 3 pixels wide. An

example is shown in Fig. 3(c).

2.3.4 Generate Candidate Regions

In this step, we eliminate any connected components

with an area less than 30% of the target object’s size

in the last frame before tracking was suspended, then

we ﬁnd the rectangular bounding box of each surviv-

ing connected component. If no candidate regions re-

main, we continue to the next frame. Example surviv-

ing bounding boxes are shown in Fig. 3(d) and over-

laid on the original image in Fig. 3(e).

2.3.5 Finding Most Similar Region

In this step, we obtain the color histogram of each re-

gion surviving the previous step and compare with the

appearance model H

using Equation 1. If the small-

est distance is below the adaptive threshold calculated

in the tracking phase, we reinitialize CAMSHIFT us-

ing the corresponding best region. An example of suc-

cessful reinitialization is shown in Fig. 3(f).

3 EXPERIMENTAL RESULTS

In this section, we evaluate the proposed method

in terms of tracking accuracy and real time perfor-

mance.

We acquired four 30 fps videos at a resolution

of 640×480 simulating target pursuit scenarios in

various outdoor locations at the Asian Institute of

Technology. All four scenes were scenes in which

CAMSHIFT is mostly successful at tracking so long

as the target is clearly visible in the scene.

In the ﬁrst frame of each video (R1 in Fig. 4),

we initialized CAMSHIFT tracking by selecting the

human target in the scene (R2 in Fig. 4). We then

ran the proposed tracking, suspension, and redetec-

tion method to the end of each video.

3.1 Accuracy

During tracking, we incrementally updated the mean

and standard deviation σ

of the distance d

be-

tween the appearance model H

and the tracked tar-

get’s color histogram H

. In almost all cases, when

the target left the scene, the distance d

exceeded the

adaptive threshold, except for a few cases in which

the redetection algorithm found a sufﬁciently similar

object in the background.

R3 to R6 in Fig. 4 show results of the backpro-

jection, binarization, and candidate region calculation

steps for one frame of each video in which the target

was not in the scene. In these four cases, the method

was successful at suspending tracking.

R7 to R10 in Fig. 4 show results of the backpro-

jection, binarization, and candidate region calculation

steps for a frame of each video in which the target ob-

ject has reappeared. In these four cases, the method

was successful at selecting the correct target region.

R11 of Fig. 4 shows successful reinitialization of

CAMSHIFT in the respective frames.

Over the four videos, the target was successfully

tracked in 95.6% of the frames in which the target

was in the scene, with false positives only 4.4% of the

frames in which the target was not in the scene. The

accuracy results per video are summarized in Table 1.

3.2 Real-time Performance

We tested the runtime performance of the system on

two different hardware conﬁgurations, a 2.26 GHz In-

tel Core i3 laptop running 32-bit Ubuntu Linux 11.10

and a 1.6 GHz Intel Atom N280 single core net-

book running 32-bit Lubuntu 11.10. We measured

the average time required for tracking (the standard

CAMSHIFT routine plus histogram distance mea-

surement and adaptive threshold update) and target

redetection over all relevant frames in the four test

videos.

The results are summarized in Table 2. Both algo-

rithms run at high frame rates, with the worst case of

just over 10 fps for redetection on the Atom proces-

sor. The method is clearly feasible for onboard exe-

cution by a mobile robot with modest computational

resources.

4 CONCLUSIONS

We have proposed and demonstrated the feasibility of

an intelligent but efﬁcient method to suspend color

histogram based trackers such as CAMSHIFT when

the target leaves the scene or is occluded in cluttered

environments. The method correctly and quickly re-

detects the target and reinitializes tracking when the

target reappears in the scene. The method is sufﬁ-

ciently fast to run on embedded systems with modest

resources, e.g., mobile robots.

We introduced a method in order to easily de-

ploy it in the real world physical robots and perform

FastTargetRedetectionforCAMSHIFTusingBack-projectionandHistogramMatching

511

R1: Frame before tracking

initialization

R2: Tracking while target is in

the scene

R3: Target leaves the scene

R4: Backprojection

R5: Binarized connected

components

R6: Candidate regions (none

are selected)

R7: Target object returns to the

scene

R8: Backprojection

R9: Binarized connected

components

R10: Selected candidate

regions (target is selected)

R11: CAMSHIFT

reinitialization

Figure 4: Proposed method tested in different outdoor environments with different background and target objects. Each

column shows images from a different video. Rows show the results of each step of processing. Blue colored rows show

processing when the target is not in the scene. Yellow colored rows show the same processing steps when the target returns to

the scene.

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Table 1: Accuracy results. For each video, we report the effective histogram distance threshold at the end of the video, the

percentage of frames containing the target in which tracking was incorrectly suspended or resumed late, and the percentage

of frames not containing the target in which the target was falsely detected.

Video Adaptive

his-

togram

thresh-

old

Object

visible (#

of frames)

Object

invisible

(# of

frames)

Object not

detected

(FN)

False

object

detection

(FP)

a 0.8299 389 50 2.23% 2.01%

b 0.6017 637 35 10.15% 0%

c 0.7432 541 150 1.20% 2.34%

d 0.7805 426 174 2.64% 3.42%

Table 2: Runtime performance of tracking and redetection

algorithms on two different processors.

Redetection Phase Tracking Phase

Core i3 Atom N280 Core i3 Atom N280

41.455 ms 91.232 ms 16.340 ms 49.234ms

real-world experiments. Robots usually lose target

while tracking because of jerky motions and irregular

(nonuniform) earth surface. Computationally expen-

sive algorithms cannot be applied on each frame to

ﬁnd the target especially when real time response is

required. Our main focus is to correctly suspend and

reinitialize CAMSHIFT tracking instead of its perfor-

mance. We can improve the performance of the visual

tracker by incorporating robust methods while keep-

ing the speed performance in view.

The proposed method is not strongly coupled to

CAMSHIFT — it could be integrated with any feature

histogram based tracker for which backprojection is

efﬁcient. Similar to work on improving CAMSHIFT

in the literature (Zhou et al., 2009), (Nouar et al.,

2006) and (Emami and Fathy, 2011), the tracking re-

sults could be improved by using a more sophisticated

appearance model.

ACKNOWLEDGEMENTS

This research was supported by a Royal Thai Govern-

ment research grant to MND and PL. AB was sup-

ported by graduate fellowships from the University of

Balochistan Quetta, the Higher Education Commis-

sion of Pakistan, and the Asian Institute of Technol-

ogy Thailand.

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