Online visual tracking based on updating with smoothing

Jin Zhang; Kai Liu; Fei Cheng; YunSong Li

doi:10.1117/12.2053153

22 May 2014 Online visual tracking based on updating with smoothing

Jin Zhang, Kai Liu, Fei Cheng, YunSong Li

Proceedings Volume 9124, Satellite Data Compression, Communications, and Processing X; 91241F (2014) https://doi.org/10.1117/12.2053153
Event: SPIE Sensing Technology + Applications, 2014, Baltimore, MD, United States

Abstract

Visual tracking is an important task in computer vision. Despite many researches have been done in this area, some problems remain. One of the problems is drifting. To handle the problem, a new appearance model update method based on a forward filtering backward smoothing particle filter is proposed in this paper. A smoothing of previous appearance model is performed by exploiting information of current frame instead of updating instantly in traditional tracking methods. It has been shown that smoothing based on future observations makes previous and current predictions more accurate, thus the appearance model update by our approach is more accurate. And at the same time, online tracking is achieved compared with some previous work in which the smoothing is done in an offline way. With the smoothing procedure, the tracker is more accurate and less likely to drift than traditional ones. Experimental results demonstrate the effectiveness of the proposed method.

Citation Download Citation

Jin Zhang, Kai Liu, Fei Cheng, and YunSong Li "Online visual tracking based on updating with smoothing", Proc. SPIE 9124, Satellite Data Compression, Communications, and Processing X, 91241F (22 May 2014); https://doi.org/10.1117/12.2053153

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KEYWORDS

Optical tracking

Particles

Visual process modeling

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Astatine

Computer vision technology

Machine vision

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