An improved interest point matching algorithm for human body tracking

The interest point (IP) matching algorithms match the points either locally or spatially. We propose a local-spatial IP matching algorithm usable for articulated human body tracking. The local-based stage finds matched IP pairs of two reference and target IP lists using a local-feature-descriptors-based matching method. Then, the spatial-based stage recovers more matched pairs from the remaining unmatched IPs through based on the result of the previous stage using the Shape Contexts (SC) feature vectors. The proposed approach benefits from the speed of local matching algorithms as well as the accuracy and robustness of spatial matching methods. Experimental results show that not only the proposed algorithm increases the precision rate from 44.71% to 97.41%, but also it improves the recall rate from 80.88% to 84.96%.