Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Object Tracking with the Multi-Templates Regression Model Based MS Algorithm

  • Zhang, Hua (College of Electrical and Electronic Engineering, Shijiazhuang University of Applied Technology) ;
  • Wang, Lijia (Dept. of Intelligent Manufacture, Hebei College of Industry and Technology)
  • Received : 2017.04.18
  • Accepted : 2017.06.21
  • Published : 2018.12.31
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Abstract

To deal with the problems of occlusion, pose variations and illumination changes in the object tracking system, a regression model weighted multi-templates mean-shift (MS) algorithm is proposed in this paper. Target templates and occlusion templates are extracted to compose a multi-templates set. Then, the MS algorithm is applied to the multi-templates set for obtaining the candidate areas. Moreover, a regression model is trained to estimate the Bhattacharyya coefficients between the templates and candidate areas. Finally, the geometric center of the tracked areas is considered as the object's position. The proposed algorithm is evaluated on several classical videos. The experimental results show that the regression model weighted multi-templates MS algorithm can track an object accurately in terms of occlusion, illumination changes and pose variations.

Keywords

E1JBB0_2018_v14n6_1307_f0001.png 이미지

Fig. 1. The flow chart of the regression model based MS algorithm.

E1JBB0_2018_v14n6_1307_f0002.png 이미지

Fig. 2. The tracking results obtained by using the MIL, CT, VTD, and the proposed method.

Table 1. The average per-frame computational cost (in seconds) of the trackers MIL, CT, VTD, and the proposed tracker

E1JBB0_2018_v14n6_1307_t0001.png 이미지

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