Recognizing Human Activities by Key Frame in Video Sequences

This paper presents a new method of human activity recognition, which is based on R transform and dynamic time warping (DTW) after the key frame is extracted from a cycle. For a key binary human silhouette, R transform is employed to represent low-level features. The advantage of the R transform lies in its low computational complexity and geometric invariance. The DTW distance based on the extracted features are calculated and compared similarities to recognize activities. Compared with other methods, ours is superior because the descriptor is robust to frame loss in the video sequence, disjoint silhouettes and holes in the shape, and thus achieves better performance in similar activities recognition, simple representation, computational complexity and template generalization. Sufficient experiments have proved the efficiency.