Human action categories using motion descriptors

In this paper, we recognize human action based on an improved BOW model and latent topic model. We proposed an improved motion descriptor to build our bag of words, which is called the local spatial-temporal maximum value of optical flow. We force similar local features that appear in different positions on the image grid to be assigned to different visual words. This approach assigns the spatial information to each visual word. Then, we use the topic model of pLSA (probabilistic Latent Semantic Analysis) to classify. Our approach is tested on two datasets, the KTH datasets and WEIZMANN datasets. The result shows our method is effective.