Recognizing human actions using curvature estimation and NWFE-based histogram vectors
This paper presents a novel scheme for human action recognition. First of all, we employ the curvature estimation to analyze human posture patterns and to yield the discriminative feature sequences. The feature sequences are further represented into sets of strings. Consequently, we can solve human action recognition problem by the string matching technique. In order to boost the performance of string matching, we apply the NonparametricWeighted Feature Extraction (NWFE) to compact the string representation. Finally, we train a Bayes classifier to perform action recognition. Unlike traditional approaches using the nearest neighbor rule, our proposed scheme can classify the human actions more efficiently while maintaining high accuracy. The experiment results show that the proposed scheme is efficient and accurate in human action recognition.