Real-Time Tracking Algorithm Based on Improved Mean Shift and Kalman Filter

摘要：

In traditional Mean Shift algorithm, color histograM is usually used as the features vectors, and the dissimilarity between the referenced targets and the target candidates is expressed by the metric derived from the Bhattacharyya coefficients. The traditional Mean Shift procedure is used to find the real position of the target by looking for the regional minimum of the distance function iteratively. While the targets color is similar to the background, the algorithm will miss the target This paper presents a new mean shift algorithm based on spatial edge orientation histograms, using space distribution and texture information as matching information. Meanwhile a Kalman filter will be used to predict the targets position. Experimental results demonstrate that the proposed algorithm can deal with intricate conditions, such as significant clutter, partial occlusions, and it can track objects efficiently and robustly.

关键词： Kalman filter Edge orientation histogram Object tracking Mean Shift Background Clutter Occlusion

作者: Dayuan Zhuang Xiaohu Ma Yunlong Xu

作者单位: School of Computer Science and Technology, Soochow University Suzhou, China

会议类型: 国际会议

会议名称: 2010 International Conference on Image Analysis and Signal Processing(2010 图像分析与信号处理国际会议 IASP 10)

会议地点: 厦门

会议语种:英文

页码: 100-103

在线出版日期: 2010-04-12（万方平台首次上网日期，不代表论文的发表时间）