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- W1972444682 abstract "One framework that fuses features for abnormal behavior recognition in videos was proposed, which provides increased robustness to noise and pose variation. First, ROI division of suspected pedestrian is achieved by optical flow method, which greatly reduces the dimensions of training data. After that, the proposed method uses the adaboost training to recognize pedestrian effectively. To capture the imaging variations and attributes of individuals, we use types of features: center track and inclination angle. Finally, D-S evidence theory is used to combine these features, which aims to recognize the abnormal behavior correctly. The results demonstrate that the recognition rate can be improved by the fusion of features." @default.
- W1972444682 created "2016-06-24" @default.
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- W1972444682 date "2013-05-01" @default.
- W1972444682 modified "2023-09-24" @default.
- W1972444682 title "Abnormal Behavior Recognition Based on Features Fusion" @default.
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- W1972444682 doi "https://doi.org/10.4028/www.scientific.net/amr.694-697.1949" @default.
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