Infrared Technology, Volume. 45, Issue 2, 137(2023)
Pedestrian Detection Method Based on Improved ViBe and YOLO v3 Algorithms
[1] [1] Sobral A, Vacavant A. A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos[J]. Computer Vision and Image Understanding, 2014, 122(5): 4-21.
[2] [2] Barnich O, Van Droogenbroeck M. ViBe: a universal background subtraction algorithm for video sequences[J]. IEEE Transactions on Image Processing, 2011, 20(6): 1709-1724.
[6] [6] HAN X, GAO Y, LU Z, et al. Research on moving object detection algorithm based on improved three frame difference method and optical flow[C]//Fifth International Conference on Instrumentation & Measurement, 2015: 580 - 584.
[10] [10] Girshick R. Fast R-CNN[C]//IEEE International Conference on Computer Vision, 2015: 1440-1448.
[11] [11] Redmon J Farhadi A. YOLO v3 anincremental improvement[EB/OL]. [2018-04-08][2022-02-08]. https://arxiv.org/abs/1804.02767.
[12] [12] HUANG G, LIU Z, Weinberger K Q, et al. Densely connected convolutional networks[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2017: 2261-2269.
[13] [13] HE K M, ZHANG X Y, REN S Q, et al. Spatial pyramid pooling in deep convolutional networks for visual Recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1904-1916.
[14] [14] PETS2006. http://www.cvg.reading.ac.uk/PETS2006/data.html[DB/OL]. 2006.
[16] [16] STAUFFER C, GRIM SON W. Learning patterns of activityusing real-time tracking[J]. IEEE Translations on Pattern Analysis and Machine Intelligence, 2000, 22(8): 747-757.
[17] [17] Goyette N, Jodoin P M, Porikli F, et al. Change detection.net: a new change detection benchmark dataset[C]//Computer Vision and Pattern Recognition Workshops, 2012: 1-8.
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LI Shiji, LI Zhongmin, LI Wei. Pedestrian Detection Method Based on Improved ViBe and YOLO v3 Algorithms[J]. Infrared Technology, 2023, 45(2): 137
Received: Mar. 18, 2022
Accepted: --
Published Online: Mar. 20, 2023
The Author Email: Zhongmin LI (zhongmli@163.com)
CSTR:32186.14.