[1] [1] HORN B K P, SCHUNCK B G. “Determining optical flow”: a retrospective ［J］. Artificial Intelligence, 1993, 59(1/2): 81-87.

[2] [2] RAMYA P, RAJESWARI R. A modified frame difference method using correlation coefficient for background subtraction ［J］. Procedia Computer Science, 2016, 93: 478-485.

[6] [6] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks ［J］. Communications of the ACM, 2017, 60(6): 84-90.

[7] [7] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: Unified, real-time object detection ［C］//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, Nevada, USA: IEEE, 2016: 779-788.

[9] [9] HOFMANN M, TIEFENBACHER P, RIGOLL G. Background segmentation with feedback: the pixel-based adaptive segmenter ［C］//Proceedings of 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Providence, RI, USA: IEEE, 2012: 38-43.

[10] [10] KRYJAK T, KOMORKIEWICZ M, GORGON M. Real-time foreground object detection combining the PBAS background modelling algorithm and feedback from scene analysis module ［J］. International Journal of Electronics and Telecommunications, 2014, 60(1): 61-72.

[11] [11] MUCHTAR K, RAHMAN F, CENGGORO T W, et al. An improved version of texture-based foreground segmentation: block-based adaptive segmenter ［J］. Procedia Computer Science, 2018, 135: 579-586.

[13] [13] MILLA J M, TORAL S L, VARGAS M, et al. Dual-rate background subtraction approach for estimating traffic queue parameters in urban scenes ［J］. IET Intelligent Transport Systems, 2013, 7(1): 122-130.

[15] [15] WU Y, LIM J, YANG M. Object tracking benchmark ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1834-1848.

[16] [16] OUYANG L C, WANG H L. Vehicle target detection in complex scenes based on YOLOv3 algorithm ［J］. IOP Conference Series: Materials Science and Engineering, 2019, 569(5): 052018.