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Journal for Studies in Management and Planning

Available at

http://edupediapublications.org/journals/index.php/JSMaP/

e-ISSN: 2395-0463

Volume 02 Issue 12

December 2016

Available online: http://edupediapublications.org/journals/index.php/JSMaP/ P a g e | 111

Non-Rigid Object Contour Tracking via a Novel Supervised

Level Set Model

Digoju Pradeepkumar & Mrs.G.Satya Prabha

1PG Scholar, Department of ECE, SLC's Institute of Engineering and Technology, Piglipur

Village, Hayathnagar Mandal, Near Ramoji Film City, Ranga Reddy District, Hyderabad,

Telangana

2Assosciate Professor, Department of ECE, SLC's Institute of Engineering and Technology,

Piglipur Village, Hayathnagar Mandal, Near Ramoji Film City, Ranga Reddy District,

Hyderabad, Telangana

Abstract— We present a novel approach to

non-rigid objects contour tracking in this

paper based on a supervised level set model

(SLSM). In contrast to most existing trackers

that use bounding box to specify the tracked

target, the proposed method extracts the

accurate contours of the target as tracking

output, which achieves better description of

the non-rigid objects while reduces

background pollution to the target model.

Moreover, conventional level set models

only emphasize the regional intensity

consistency and consider no priors.

Differently, the curve evolution of the

proposed SLSM is object-oriented and

supervised by the specific knowledge of the

targets we want to track. Therefore, the

SLSM can ensure a more accurate

convergence to the exact targets in tracking

applications. In particular, we firstly

construct the appearance model for the

target in an online boosting manner due to

its strong discriminative power between the

object and the background. Then, the learnt

target model is incorporated to model the

probabilities of the level set contour by a

Bayesian manner, leading the curve

converge to the candidate region with

maximum likelihood of being the target.

Finally, the accurate target region qualifies

the samples fed to the boosting procedure as

well as the target model prepared for the

next time step. We firstly describe the

proposed mechanism of two-phase SLSM for

single target tracking, then give its

generalized multi-phase version for dealing

with multi-target tracking cases. Positive

decrease rate is used to adjust the learning

pace over time, enabling tracking to

continue under partial and total occlusion.

Experimental results on a number of

challenging sequences validate the

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Journal for Studies in Management and Planning

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http://edupediapublications.org/journals/index.php/JSMaP/

e-ISSN: 2395-0463

Volume 02 Issue 12

December 2016

Available online: http://edupediapublications.org/journals/index.php/JSMaP/ P a g e | 112

effectiveness of the proposed method. Index

Terms— Object tracking, level sets, curve

evolution, boosting, appearance modeling.

I. INTRODUCTION

Object tracking, which refers to the task of

generating the trajectories of the moving

objects in a sequence of images, is a

challenging research topic in the field of

computer vision. The problem and its

difficulty depend on several factors, such as

the amount of prior knowledge about the

target object and the number and type of

parameters being tracked, e.g., location,

scale, detailed contour. Although there has

been some success with building trackers for

specific object classes, Manuscript received

November 19, 2014; revised May 1, 2015

and May 26, 2015; accepted June 7, 2015.

Date of publication June 18, 2015; date of

current version July 7, 2015. This work was

supported in part by the National Natural

Science Foundation of China under Grant

61472103 and Grant 61300111 and in part

by the Key Program under Grant 61133003.

The associate editor coordinating the review

of this manuscript and approving it for

publication was Prof. Kiyoharu Aizawa.

(Corresponding author: Hongxun Yao.) The

authors are with the Department of

Computer Science and Technology, Harbin

Institute of Technology, China (e-mail:

sunxintyc@ 163.com; h.yao@hit.edu.cn;

s.zhang@hit.edu.cn; lidonghit@hit.edu.cn).

Color versions of one or more of the figures

in this paper are available online at

http://ieeexplore.ieee.org. Digital Object

Identifier 10.1109/TIP.2015.2447213

tracking generic real-world objects has

remained challenging due to unstable

lighting condition, pose variations, scale

changes, view-point changes, and camera

noise etc. Early tracking methods use fixed

appearance model to describe the target,

which are unable to successfully track the

target over long time. To overcome this

drawback, some tracking algorithms try to

update the target appearance over time in an

online manner. The appearance models

adopted by these methods include histogram

subspace models [4] as well as sparse

representation models . Besides, some

researchers resort to adopting discriminative

learning methods to make the trackers easy

to distinguish the target from its

background. The methods based on boosting

and SVMs show impressive performance

and attract much attention. In contrast with

constructing two separate models for the

target and background respectively,

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e-ISSN: 2395-0463

Volume 02 Issue 12

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classifier learning based approaches are

more inclined to seize properties of most

discrimination between them. Despite

having the promising performance, these

traditional trackers face a practical problem

that they use rectangular bounding box or

oval to approximate the tracked target.

However, objects in practice may have

complex shapes that cannot be well

described by simple geometric shapes, see

Fig. 1(a) for some examples. Since the

rectangle box used for presenting the tracked

target directly determines the samples to be

extracted in the subsequent target

appearance modeling/unpdate step, it is a

critical factor to tracking performance.

Inaccurate target presentation easily results

in performance loss due to the pollution of

non-object regions residing inside the

rectangle box. In order to better fit the object

shape, some methods adopt the scale

selection mechanism that aims to search for

the best scale that covers the target

accurately. An intuitive idea is to run the

algorithm in different scales, then select the

one maximizing the object function of the

tracking algorithm. Further, this selection

mechanism is also extended to orientation.

By simultaneously controlling both the scale

and orientation, the statistic bias for the

target distribution can be controlled, see

Fig.1(a), and this, to some extent, makes

better target description and tracking

estimation. Nevertheless, all these

scale/orientation adjustments are still based

on simple geometric shapes (such as

rectangle and oval), which will inevitably

introduce a large number of background

pixels when used for presenting real-world

object with complex shapes. Ideally, a better

manner to describe the target is to use the

accurate contour along the target’s surface.

Fig. 1. Motivation: (a) shows the typical

bounding box presentation on complex

object with scale/orientation adaption. (b)-

(g) gives some contour tracking examples of

the proposed method in various challenging

cases, whose frame numbers are 511, 14,

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