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14 August 2019 Multi-target tracking of surveillance video with differential YOLO and DeepSort
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Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019); 111792L (2019) https://doi.org/10.1117/12.2540269
Event: Eleventh International Conference on Digital Image Processing (ICDIP 2019), 2019, Guangzhou, China
Abstract
With the shortcomings of traditional algorithm in video surveillance on low accuracy, poor robustness and unable achieved real-time tracking for multi-targets, this paper presents a Multi-target tracking algorithm, DeepSort, on the base of deep neural network to achieve the end-to-end surveillance video multi-personal target real-time detection and tracking. The high accuracy of target detection by YOLO algorithm provides DeepSort with weaker dependence on detection results, lower interference of occlusion and illumination and improved tracking robustness. Moreover, due to the high redundancy of the surveillance video itself, the difference filter is used to screen the video frames with no foreground targets and small changes, so as to reduce the detection cost and improve the detection and tracking speed. The experimental evaluation of the video surveillance dataset NPLR, the average MOTA of this algorithm is 68.7, the highest value is 86.8; the average speed is 81.6Hz, the highest value is 140Hz. It shows that the end-to-end algorithm is feasible and effective.
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Xu Zhang, Xiangyang Hao, Songlin Liu, Junqiang Wang, Jiwei Xu, and Jun Hu "Multi-target tracking of surveillance video with differential YOLO and DeepSort", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111792L (14 August 2019); https://doi.org/10.1117/12.2540269
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