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- W2267279275 abstract "Abstract During recent years, the demand for automated video surveillance has become the subject of much attention. Currently, video surveillance is an essential part of security and monitoring in banks, streets, buildings, department stores, stadiums, highways, railway stations, and crowded gatherings. One important reason for using video surveillance is to counter terrorism. In this context, a multi-camera handoff system for person re-identification using a color-based global appearance model is presented. We address two main factors that affect re-identification performance in this paper: (1) robust features (viewpoint orientation and indoor/outdoor cinematography) and (2) re-identification or classification. A hexagonal-superpixel method is introduced to minimize the false recognition rate due to the above-mentioned challenges. First we detect the human or object in a video and use a mixture of Gaussian models extended with a trajectory-learning algorithm to solve the trajectory dilemma. Second, a novel feature descriptor is proposed in which sensitive features are extracted using the proposed Color Hexagonal-SIFT and Color Histogram Features (CHF) methods. Third, we generate a combined appearance feature descriptor for re-identifying the human in different scenes. Finally, we introduce a Time-Tree-based learning method to minimize the gallery set׳s overhead and increase the accuracy of the re-identification results. The proposed framework is tested on videos taken using three cameras placed in three different locations. We also tested this framework on publically available data from the i-LIDS and VIPeR datasets. The results show outstanding speed and re-identification accuracy in dealing with the above-mentioned issues." @default.
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- W2267279275 date "2016-05-01" @default.
- W2267279275 modified "2023-09-24" @default.
- W2267279275 title "Multi-camera handoff for person re-identification" @default.
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- W2267279275 doi "https://doi.org/10.1016/j.neucom.2016.01.037" @default.
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