IMM/MHT FUSING FEATURE INFORMATION IN VISUAL TRACKING  

作　　者：Li Shuangquan Sun Shuyan Jiang Sheng Huang Zhipei Wu Jiankang 

机构地区：[1]School of Information Science and Engineering, Graduate University of Chinese Academy of Sciences, Beijing 100190, China

出　　处：《Journal of Electronics(China)》2009年第6期765-770,共6页电子科学学刊（英文版）

基　　金：Supported by the National Natural Science Foundation of China (No. 60772154);the President Foundation of Graduate University of Chinese Academy of Sciences (No. 085102GN00)

摘　　要：In multi-target tracking,Multiple Hypothesis Tracking (MHT) can effectively solve the data association problem. However,traditional MHT can not make full use of motion information. In this work,we combine MHT with Interactive Multiple Model (IMM) estimator and feature fusion. New algorithm greatly improves the tracking performance due to the fact that IMM estimator provides better estimation and feature information enhances the accuracy of data association. The new algorithm is tested by tracking tropical fish in fish container. Experimental result shows that this algorithm can significantly reduce tracking lost rate and restrain the noises with higher computational effectiveness when compares with traditional MHT.In multi-target tracking, Multiple Hypothesis Tracking （MHT） can effectively solve the data association problem. However, traditional MHT can not make full use of motion information. In this work, we combine MHT with Interactive Multiple Model （IMM） estimator and feature fusion. New algorithm greatly improves the tracking performance due to the fact that IMM estimator provides better estimation and feature information enhances the accuracy of data association. The new algo- rithm is tested by tracking tropical fish in fish container. Experimental result shows that this algorithm can significantly reduce tracking lost rate and restrain the noises with higher computational effec- tiveness when compares with traditional MHT.

关 键 词：Multiple Hypothesis Tracking （MHT） Interacting Multiple Model （IMM） Feature information fusion Data association 

分 类 号：TP317[自动化与计算机技术—计算机软件与理论] TN953[自动化与计算机技术—计算机科学与技术]