Human Tracking Using Improved Sample-Based Joint Probabilistic Data Association Filter
نویسندگان
چکیده
The human tracking problem is a hot issue in human-robot interaction, in which a conventional algorithm sample-based joint probabilistic data association filters (SJPDAF) is widely used. In this paper, the algorithm is first extended to the situation of multi-sensor fusion and then accelerated to promote the real-time performance. The simulation and experiments on robots both show good results, reflecting the robust and the accuracy of our improved SJPDAF.
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تاریخ انتشار 2012