CamShift Tracking Method Based on Target Decomposition

Chunbo Xiu,Yi Cheng,Huixin Tian,Rongfeng Wan,Jing Luo,Shian Wei

doi:10.1155/2015/524120

Chunbo Xiu, Yi Cheng + Show 4 more

Open Access

https://doi.org/10.1155/2015/524120

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Abstract

In order to avoid the inaccurate location or the failure tracking caused by the occlusion or the pose variation, a novel tracking method is proposed based on CamShift algorithm by decomposing the target into multiple subtargets for location separately. Distance correlation matrices are constructed by the subtarget sets in the template image and the scene image to evaluate the correctness of the location results. The error locations of the subtargets can be corrected by resolving the optimization function constructed according to the relative positions among the subtargets. The directions and sizes of the correctly located subtargets with CamShift algorithm are updated to reduce the disturbance of the background in the tracking progress. Simulation results show that the method can perform the location and tracking of the target and has better adaptability to the scaling, translation, rotation, and occlusion. Furthermore, the computational cost of the method increases slightly, and its average tracking computational time of the single frame is less than 25 ms, which can meet the real-time requirement of the TV tracking system.

Highlights

Target tracking is a key technology in computer vision field
The feasibility and effectiveness of the proposed method is shown by the comparative analysis with the basic CamShift algorithm and the improved CamShift algorithm based on multifeature fusion [18]
In order to meet the requirement of the target precise location and tracking in the complex cases, the tracking strategy based on the target decomposition is proposed

Summary

Introduction

It can be widely applied to industry, surveillance, robotics, human machine interface, and so forth [1,2,3,4]. It is a challenging task in real-world scenarios to track a target precisely due to the variability of the target’s movements, shapes, clutter, and occlusions. The size and shape of the target in the current scene may be different from those in the previous one or the target template. Complex background affects the tracking performance badly. It is difficult to distinguish the target from the background with the similar color

Methods

Results

Conclusion