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One example based action detection in hough space
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AI
DOI：10.1007/s11042-013-1478-9.png)
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Abstract
En 中文
Given a short action query video, to detect the same category action in a target video is a very important research topic. We propose a fast action detection method motivated by the idea of Hough Transformation. First, we extract the HOG features at the corner points from the query video. The corner points are referred to as interest points. Then, video clips are formed by sliding a window on the query video. For each T frames of a clip, in the displacement Hough space, the interest points in all of the frames are matched with the interest points in the first frame. We count the matched pairs in the cells of the Hough space to form a 2d displacement histogram. The query video is represented by a 2d displacement histogram sequence. After that, we divide the target video with motion into video cubes. These video cubes are similarly represented by displacement histogram sequences. The matrix cosine similarity is used to compute the similarities between the query video and the video cubes. This process is referred to as action matching. In the end, with the action matching results, we precisely localize the action using the locations of the matched interest points. Our key contribution is that we propose a very simple and fast algorithm that represents the actions as the displacement histogram sequences. Experiments on the challenging datasets containing both of the simple and realistic backgrounds confirm the effectiveness and efficiency of our method.
Keywords:
Action detection
Hough voting
Displacement histogram
Matrix cosine similarity
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