[1]

赵潇. 基于人类视觉系统的监控视频目标提取技术研究[D]. 重庆: 重庆邮电大学, 2018.

[2]

DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C]//2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. USA: IEEE, 2005: 886-893.

[3]

VIOLA P, JONES M. Rapid object detection using a boosted cascade of simple features[C]//Proceedings the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. USA: IEEE, 2001: 511-518.

[4]

GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. USA: IEEE, 2014: 580-587.

[5]

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

[6]

LIU W, ANGUELOV D, ERHAN D, et al. SSD: Single shot multibox detector[C]//European Conference on Computer Vision. Cham: Springer, 2016: 21-37.

[7]

LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]//Proceedings of the IEEE International Conference on Computer Vision. Italy: IEEE, 2017: 2980-2988.

[8]

REDMON J, FARHADI A. YOLO9000: Better, faster, stronger[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. USA: IEEE, 2017: 7263-7271.

[9]

REDMON J, FARHADI A. Yolov3: An incremental improvement[EB/OL]. arxiv. org, (2018-04-10)[2020-11-01]. https://arxiv.org/pdf/1804.02767.pdf.

[10]

IANDOLA F N, HAN S, MOSKEWICZ M W, et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size[EB/OL]. arxiv. org, (2016-03-20)[2020-11-01]. https://arxiv.org/pdf/1602.07360v3.pdf.

[11] STAUFFER C, GRIMSON W E L.  Learning patterns of activity using real-time tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 747-757.   doi: 10.1109/34.868677
[12]

ZIVKOVIC Z. Improved adaptive Gaussian mixture model for background subtraction[C]//Proceedings of the 17th International Conference on Pattern Recognition. UK: IEEE, 2004: 28-31.

[13] ZIVKOVIC Z, VAN DER HEIJDEN F.  Efficient adaptive density estimation per image pixel for the task of background subtraction[J]. Pattern Recognition Letters, 2006, 27(7): 773-780.   doi: 10.1016/j.patrec.2005.11.005
[14] LEE D S.  Effective Gaussian mixture learning for video background subtraction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(5): 827-832.   doi: 10.1109/TPAMI.2005.102
[15] 龚安, 牛博, 史海涛.  基于分块的帧差法和混合高斯算法的油田作业区入侵检测[J]. 计算机与数字工程, 2019, 47(12): 3041-3044.
[16]

刘馨. 监控视频中的图像颜色评价与优化[D]. 杭州: 浙江大学, 2015.

[17] 李均, 王志诚, 吴雨轩, 等.  熵概念的延拓——从热熵到信息熵[J]. 大学物理, 2020, 39(10): 29-33.
[18] 王林, 王超凡.  差分信息熵在拼接图像质量评估中的应用[J]. 计算机仿真, 2020, 37(4): 265-268, 273.   doi: 10.3969/j.issn.1006-9348.2020.04.055
[19]

HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. USA: IEEE, 2016: 770-778.

[20]

汪斌, 陈宁. 基于残差注意力U-Net结构的端到端歌声分离模型[J]. 华东理工大学学报(自然科学版). doi: 10.14135/j.cnki.1006-3080.20200903001.

[21] 高磊, 范冰冰, 黄穗.  基于残差的改进卷积神经网络图像分类算法[J]. 计算机系统应用, 2019, 28(7): 139-144.
[22] LECUN Y, BOTTOU L, BENGIO Y, et al.  Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.   doi: 10.1109/5.726791
[23] KRIZHEVSKY A, SUTSKEVER I, HINTON G E.  Imagenet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90.   doi: 10.1145/3065386
[24]

ZEILER M D, FERGUS R. Visualizing and understanding convolutional networks[C]//European Conference on Computer Vision. Cham: Springer, 2014: 818-833.

[25]

SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[EB/OL]. arxiv. org, (2014-10-22)[2020-11-01]. https://arxiv.org/abs/1409.1556.