An improved Normalized Cross Correlation algorithm for object tracking
Legong Sun, Mao Zheng · 2010
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.