[1] TRIGGS B, DALAL N. Histograms of oriented gradients for human detection[C]//Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2005:886-893. [2] LOWE D G. Object recognition from local scale-invariant features[C]//Proceedings of the 7th IEEE International Conference on Computer Vision. Piscataway, NJ:IEEE, 1999:1150-1157. [3] 郭爱心,殷保群,李运.基于深度卷积神经网络的小尺度行人检测[J].信息技术与网络安全,2018,37(7):50-53, 57. (GUO A X, YIN B Q, LI Y. Small-size pedestrian detection via deep convolutional neural network[J]. Information Technology and Network Security, 2018, 37(7):50-53, 57.) [4] REN S, HE K, GIRSHICK R, et al. Faster R-CNN:towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6):1137-1149. [5] 陈光喜,王佳鑫,黄勇,等.基于级联网络的行人检测方法[J].计算机应用,2019,39(1):186-191. (CHEN G X, WANG J X, HUANG Y, et al. Pedestrian detection method based on cascade networks[J]. Journal of Computer Applications, 2019, 39(1):186-191.) [6] REDMON J, FARHADI A. YOLO9000:better, faster, stronger[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2017:6517-6525. [7] 徐超,闫胜业.改进的卷积神经网络行人检测方法[J].计算机应用,2017,37(6):1708-1715. (XU C, YAN S Y. Improved pedestrian detection method based on convolutional neural network[J]. Journal of Computer Applications, 2017,37(6):1708-1715.) [8] BENENSON R, OMRAN M, HOSANG J, et al. Ten years of pedestrian detection, what have we learned?[C]//Proceedings of the 2014 European Conference on Computer Vision, LNCS 8926. Berlin:Springer, 2014:613-627. [9] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 2012 International Conference on Neural Information Processing Systems. North Miami Beach, FL:Curran Associates Inc, 2012:1097-1105. [10] ZHANG L, LIN L, LIANG X, et al. Is Faster R-CNN doing well for pedestrian detection?[C]//Proceedings of the 2016 European Conference on Computer Vision, LNCS 9906. Cham:Springer, 2016:443-457. [11] APPEL R, FUCHS T, DOLLÁR P. Quickly boosting decision trees:pruning underachieving features early[C]//Proceedings of the 30th International Conference on Machine Learning.[S.l.]:JMLR, 2013, 28:Ⅲ-594-Ⅲ-602. [12] LI J, LIANG X, SHEN S, et al. Scale-aware Fast R-CNN for pedestrian detection[J]. IEEE Transactions on Multimedia, 2018, 20(4):985-996. [13] GIRSHICK. R. Fast R-CNN[C]//Proceedings of the 2015 IEEE International Conference on Computer Vision. Piscataway, NJ:IEEE, 2015:1440-1448. [14] ZHANG S, WEN L, BIAN X, et al. Single-shot refinement neural network for object detection[C]//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2018:4203-4212. [15] HOWARD A G, ZHU M, CHEN B, et al. MobileNets:efficient convolutional neural networks for mobile vision applications[J]. arXiv E-print, 2017:arXiv:1704.04861. [16] LIU W, ANGUELOV D, ERHAN D, et al. SSD:single shot multibox detector[C]//Proceedings of the 2016 European Conference on Computer Vision, LNCS 9905. Cham:Springer, 2016:21-37. [17] WOJEK C, DOLLAR P, SCHIELE B, et al. Pedestrian detection:an evaluation of the state of the art[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(4):743-761. |