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A novel statistical framework for modeling the intrinsic structure of crowded scenes and detecting abnormal activities is presented in this paper. The proposed framework essentially turns the anomaly detection process into two parts, namely, motion pattern representation and crowded context modeling. During the first stage, we averagely divide the spatiotemporal volume into atomic blocks. Considering...
In this paper, we propose a novel method for text detection in natural scenes. Gradient Vector Flow is first used to extract both intra-character and inter-character symmetries. In the second step, we group horizontally aligned symmetry components into text lines based on several constraints on sizes, positions and colors. Finally, to remove false positives, we employ a learning-based approach which...
We present a new video character recognition method based on hierarchical classification. In the first step, we propose a method for character segmentation of the text line detected by the text detection method. The segmentation algorithm uses dynamic programming to find least-cost paths in the gray domain to identify the spaces between characters. For the segmented characters, we get a Canny edge...
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