Human Action Recognition Based on Local Window Matching

Based on the traditional bag-of-words model, we propose a new human action recognition method based on local window matching strategy which considering the temporal and spatial constraints among human pose.Firstly, human posture difference is used as features of human action sequences.Secondly, in the training phrase, instead of the global training method the local training method is used to improve the characterization of traditional characteristic vocabulary.In the feature quantization phrase, we use the adaptive local linear reconstruction strategy to give feature vectors more flexible weight.In the object description phrase, we use the time pyramid method and the sliding window method respectively to segment the whole action sequence into a set of local motion fragments, and then features of these local motion fragments are connected to describe the whole action sequence.Finally, we use the histogram intersection operation to do feature matching work.Experiments are done on the MSR Action3 Ddatabase.The results show that our method can get better recognition ratio.