Target Tracking Based on Online Template Clustering
Received date: 2015-08-29
Revised date: 2015-12-13
Online published: 2016-07-30
The template of most tracking algorithms is difficult to suit appearance changes of non-rigid objects due to high redundancy. To deal with the problem, a tracking method with online templates clustering based on particle filter is proposed. Positive and negative template sets are established to describe the target and background. Candidates are then extracted based on the dynamic model. A likelihood function is built based on the withinclass distance between the candidate and template sets, and the between-class distance between positive sets and negative sets. The best candidate is considered the tracking result according to the maximum a posteriori probability (MAP). The main idea of online template clustering is as follows: First, the cluster radius is determined by the state class produced from a series of continuous target states in a certain range. Second, the positive template set combined with the recent several tracking results are used for clustering by using the mean shift iterative method and the above cluster radius. Third, the updated positive template set consists of the new cluster centers. In complicated situations, experiments show that this method can retain different appearance states of target and track the target accurately.
HU Zhao-hua, WANG Guan-nan, WANG Jue, SHAO Xiao-wen, BIAN Fei-fei . Target Tracking Based on Online Template Clustering[J]. Journal of Applied Sciences, 2016 , 34(4) : 430 -440 . DOI: 10.3969/j.issn.0255-8297.2016.04.008
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