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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. |
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Video
Video surveillance
Optical flow
3D modeling
Detection and tracking algorithms
Motion models
Image segmentation
