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.
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)
