Action recognition based on a selective sampling strategy for real-time video surveillance

Action recognition is a very challenging task in the field of real-time video surveillance. The traditional models on action recognition are constructed of Spatial-temporal features and Bag-of-Feature representations. Based on this model, current research work tend1s to introduce dense sampling to achieve better performance. However, such approaches are computationally intractable when dealing with large video dataset. Hence, there are some recent works focused on feature reduction to speed up the algorithm without reducing accuracy.In this paper, we proposed a novel selective feature sampling strategy on action recognition. Firstly, the optical flow field is estimated throughout the input video. And then the sparse FAST (Features from Accelerated Segment Test)points are selected within the motion regions detected by using the optical flows on the temporally down-sampled image sequences. The selective features, sparse FAST points, are the seeds to generate the 3D patches. Consequently, the simplified LPM (Local Part Model) which greatly speeds up the model is formed via 3D patches. Moreover, MBHs (Motion Boundary Histograms) calculated by optical flows are also adopted in the framework to further improve the efficiency. Experimental results on UCF50 dataset and our artificial dataset show that our method could reach more real-time effect and achieve a higher accuracy compared with the other competitive methods published recently.