An Improved Normalized Cross Correlation Algorithm for Object Tracking
This work describes a novel algorithm for real time tracking of imaging targets in video sequence. Normalized Cross Correlation based on Box-Filtering (NCC-BF) is a widely used algorithm in practice. But NCC-BF still has lots of redundancies in exhaustive template matching process. Thus we put forward a sufficient termination condition based on an adaptive lower bound threshold function in this paper, and we have proved that if this termination condition is verified, the correlation score at current position must lower than the maximum correlation obtained in previous, thus the template can be proceed with next reference position with out executing the rest of operations in current position. So the redundancy in NCC-Based object tracking can be efficiently reduced by our novel termination condition described in this paper. The experimental results of our new algorithm and actual CPU time are reported.
object tracking imaging target Box-Filtering early termination condition Normalized Cross Correlation
Legong Sun Zheng Mao
College of Electronic and Control Engineering, Beijing University of Technology, Beijing, China
国际会议
2010 IEEE 10th International Conference on Signal Processing(第十届信号处理国际会议 ICSP 2010)
北京
英文
1267-1270
2010-08-24（万方平台首次上网日期，不代表论文的发表时间）