Rapid Target Recognition and Tracking under Large Scale Variation Using Semi-Naive Bayesian

摘要：

In this paper, we present a robust feature matching-based solution to real-time target recognition and tracking under large scale variation using affordable memory consumption. In order to extract keypoints robust to scale, viewpoint changes and partial occlusions, we propose a training scheme based on FAST to detect the most repeatable features in target region. As for feature matching, Ferns suffers from unaffordable memory consumption for lower-power hardware platform, by modifying the original Ferns, we achieve comparable results with only a tiny fraction of runtime memory, which is one aspect of our contribution. To handle with long distance, large scale variation target tracking, we take advantage of multi-model tactics, which is another contribution of us. At last, a typical tracking experiment with speed over 40 fps on a 2.0 GHz PC confirms the efficiency of our approach.

关键词： Feature Detection Target Tracking Modified Ferns Multi-model

作者: SUN Kang WANG Bo HAO Zhihui

作者单位: School of Automation, Beijing Institute of Technology, Beijing 100080,P.R.China

会议类型: 国际会议

会议名称: The 29th Chinese Control Conference(第二十九届中国控制会议)

会议地点: 北京

会议语种:英文

页码: 1-5

在线出版日期: 2010-07-29（万方平台首次上网日期，不代表论文的发表时间）