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With the development of information technology, the automatic recognition of human action from video becomes a very popular research topic. In this paper, we review recent state-of-the-art of human action recognition methods in videos. First, we compare several notable handcrafted methods. Then we introduce some deep learning action recognition models. As deep learning becomes hot spot of research...
We propose a novel framework for fast and robust video anomaly detection and localization in complicated crowd scenes. Images of each video are split into cells for extracting local motion features represented by optical flow. In the train videos, most background cells are subtracted by ViBe model. Feature vectors are extracted from each cell by integrating the value of optical flow in 8 different...
In this paper, we propose a new approach to detect hand-waving motion in crowds. Different from previous approaches which are often based on segmentation and motion detection, our method can be seen as a complexity reduction process from the problem of 3D motion detection to 2D object detection. Through arranging the same row of per video frame along the time sequence, we obtain 2D images composed...
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