A spatial-temporal constraint-based action recognition method

In this paper, we propose a spatial-temporal constraint-based action recognition method, in which two actions are compared by both the appearance features and the spatial-temporal structures. To represent the appearance information in videos, we utilize a random quantization method to obtain a more precise BoW-based representation. To calculate the similarity of two videos, we match the quantized interest point sets and map the matched pairs into the spatial-temporal offset space to compare the similarities of spatial-temporal structures. We leverage the KNN model to classify the actions. The experiment results on both KTH action dataset and YouTube action dataset demonstrate the effectiveness of our proposed method on action recognition.