Visual Tracking Based on Complementary Learners with Distractor Handling

Sungshin Kim,Eun Kyeong Kim,Hansoo Lee,Suryo Adhi Wibowo

doi:10.1155/2017/5295601

Sungshin Kim, Eun Kyeong Kim + Show 2 more

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https://doi.org/10.1155/2017/5295601

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Abstract

The representation of the object is an important factor in building a robust visual object tracking algorithm. To resolve this problem, complementary learners that use color histogram- and correlation filter-based representation to represent the target object can be used since they each have advantages that can be exploited to compensate the other’s drawback in visual tracking. Further, a tracking algorithm can fail because of the distractor, even when complementary learners have been implemented for the target object representation. In this study, we show that, in order to handle the distractor, first the distractor must be detected by learning the responses from the color-histogram- and correlation-filter-based representation. Then, to determine the target location, we can decide whether the responses from each representation should be merged or only the response from the correlation filter should be used. This decision depends on the result obtained from the distractor detection process. Experiments were performed on the widely used VOT2014 and VOT2015 benchmark datasets. It was verified that our proposed method performs favorably as compared with several state-of-the-art visual tracking algorithms.

Highlights

Given the initial state of a target object in the first frame, the goal of visual tracking is to predict the states of the target in subsequent frames
A generative approach combined with an optimization method, such as the Lukas-Kanade algorithm, Kalman filter [2], and particle filter [3], was usually applied
Color histogram-based representation has advantages which are robust to deformations, it has a disadvantages or a drawback when illumination changes occur

Summary

Introduction

Given the initial state (e.g., position and other information) of a target object in the first frame, the goal of visual tracking is to predict the states of the target in subsequent frames. Visual tracking has an important role in several applications in the areas of computer vision, such as motion analysis, visual surveillance, human computer interaction, and robot navigation This issue has been studied for several decades and considerable progress has been made, it still presents challenges, in particular, the development of a robust algorithm for overcoming problems such as occlusions, camera motion, illumination changes, motion changes, and size changes. The Kalman filter has some limitations for challenging problems in that it assumes that both the system and observation model equations are linear and that the distribution of the state uses Gaussian distribution. These assumptions are not realistic in many real conditions.

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