Scale adaptive tracking using mean shift and efficient feature matching
Song, Yi, Li, Shuxiao, Zhang, Jinglan, & Chang, Hongxing (2014) Scale adaptive tracking using mean shift and efficient feature matching. In 22nd International Conference on Pattern Recognition (ICPR), IEEE, Stockholm, Sweden, pp. 2233-2238.
The mean shift tracker has achieved great success in visual object tracking due to its efficiency being nonparametric. However, it is still difficult for the tracker to handle scale changes of the object. In this paper, we associate a scale adaptive approach with the mean shift tracker. Firstly, the target in the current frame is located by the mean shift tracker. Then, a feature point matching procedure is employed to get the matched pairs of the feature point between target regions in the current frame and the previous frame. We employ FAST-9 corner detector and HOG descriptor for the feature matching. Finally, with the acquired matched pairs of the feature point, the affine transformation between target regions in the two frames is solved to obtain the current scale of the target. Experimental results show that the proposed tracker gives satisfying results when the scale of the target changes, with a good performance of efficiency.
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|Item Type:||Conference Paper|
|Keywords:||object tracking, mean shift, feature point matching, scale adaptation|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2014 IEEE|
|Copyright Statement:||Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.|
|Deposited On:||11 Feb 2015 23:23|
|Last Modified:||13 Feb 2015 20:51|
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