Visual Tracking via Spatio-temporal Context Learning using Multi-Templates
Probabilistic tracking algorithms typically using linear structure to update the learning model.Such linear structure is not appropriate for long-term robust tracking as the occlusion and other challenging factors may interfere the processing of incoming frames.Recently a spatio-temporal context(STC)algorithm based on Bayesian framework has using the context information between the target and its locally contexts to help tracking.In this paper,we propose an adaptive structure model that can help to discard the negative information during the tracking.This model establishes multi-templates to hold the credible information while tracking,when one of the templates gets the better confidence coefficient,this template will replace the current template and update the learning model.Furthermore,an improved scale update scheme is proposed to handle the scale variations problems in STC.Extensive experimental results show that our trackers superior robustness and accuracy against the original STC algorithm.
visual tracking spatio temporal context adaptive structure model multiple templates
Zhengyu Zhu Wei Zhu Shuai Li
College of Computer Science,Chongqing University;Key Laboratory of Database and Parallel Computing o College of Computer Science,Chongqing University
国际会议
重庆
英文
708-712
2017-10-03（万方平台首次上网日期，不代表论文的发表时间）