基于点轨迹的核相关滤波器跟踪算法

doi:10.11959/j.issn.1000-436x.2018097

Abstract

Abstract:

Visual tracking is one of the most important directions in computer vision.However,many state-of-the-art algorithms cannot track the interested object reliably due to occlusion during tracking process,which leads to deficiency of object information.In order to solve occlusion problem,a kernelized correlation tracking method based on point trajectories was proposed.Through analyzing long-term motion cues of the local information,point trajectories were labeled by spectral clustering.These labeled points were used to differentiate the foreground and background objects and thus detect whether the target was occluded or drifts.If drifting and occlusion occur,re-detection was used to detect the re-entering of the target.Experimental results show that the proposed algorithm can handle occlusion and drifting problems effectively.

Key words: kernelized correlation filter, point trajectories, spectral clustering

CLC Number:

TP391

Yunqiu LYU,Kai LIU,Fei CHENG. Kernelized correlation tracking based on point trajectories[J]. Journal on Communications, 2018, 39(6): 190-198.

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References 26

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算法	平均成功率	平均准确率（20px）
MCKCF	72.96%	82.63%
KCF	65.75%	68.91%
DFT	53.69%	59.63%
TLD	51.47%	52.80%

算法	Girl	Coke	Jogging	Walking2	Singer1	平均帧率
KCF	298	192	241	122	116	193.8
MCKCF	33	13	10	25	9	17.8

Kernelized correlation tracking based on point trajectories

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