[1] [1] Dollar P, Wojek C, 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.

[2] [2] Wang X Y, Han T X, Yan S C. An HOG-LBP human detector with partial occlusion handling[C]//Proceedings of the 12th IEEE International Conference on Computer Vision, 2009: 32–39.

[3] [3] Dai J F, Li Y, He K M, et al. R-FCN: object detection via region-based fully convolutional networks[C]//Proceedings of the 30th Conference on Neural Information Processing Systems, 2016: 379–387.

[4] [4] Wang K J, Zhao Y D, Xing X L. Deep learning in driverless vehicles[J]. CAAI Transactions on Intelligent Systems, 2018, 13(1): 55–69.

[5] [5] Wang Z L, Huang M, Zhu Q B, et al. The optical flow detection method of moving target using deep convolution neural network[J]. Opto-Electronic Engineering, 2018, 45(8): 180027.

[6] [6] Liu W, Anguelov D, Erhan D, et al. SSD: single shot MultiBox detector[C]//Proceedings of the 14th European Conference on Computer Vision, 2016: 21–37.

[7] [7] Ren S Q, He K M, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems, 2015: 91–99.

[8] [8] Cheng D Q, Tang S X, Feng C C, et al. Extended HOG-CLBC for pedstrain detection[J]. Opto-Electronic Engineering, 2018, 45(8): 180111.

[9] [9] Ouyang W L, Wang X G. Joint deep learning for pedestrian detection[C]//Proceedings of 2013 IEEE International Conference on Computer Vision, 2014: 2056–2063.

[10] [10] Tian Y L, Luo P, Wang X G, et al. Deep learning strong parts for pedestrian detection [C]//Proceedings of 2015 IEEE International Conference on Computer Vision, 2015: 1904–1912.

[11] [11] Ouyang W L, Zeng X Y, Wang X G. Partial occlusion handling in pedestrian detection with a deep model[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2016, 26(11): 2123–2137.

[12] [12] Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision[J]. arXiv:1512.00567v3[cs.CV], 2015.

[13] [13] Han W, Khorrami P, Le Paine P, et al. Seq-NMS for video object detection[J]. arXiv:1602.08465[cs.CV], 2016.

[14] [14] He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770–778.

[15] [15] He K M, Gkioxari G, Dollár P, et al. Mask R-CNN[C]// Proceedings of 2017 IEEE International Conference on Computer Vision, 2017: 2980–2988.

[16] [16] Dai J F, Qi H Z, Xiong Y W, et al. Deformable convolutional networks[C]//Proceedings of 2017 IEEE International Conference on Computer Vision, 2017: 764–773.

[17] [17] Bell S, Zitnick C L, Bala K, et al. Inside-outside net: detecting objects in context with skip pooling and recurrent neural networks[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016: 2874–2883.

[18] [18] Cai Z W, Fan Q F, Feris R S, et al. A unified multi-scale deep convolutional neural network for fast object detection[C]//Proceedings of the 14th European Conference on Computer Vision, 2016: 354–370.

[19] [19] Goodfellow I J, Warde-Farley D, Mirza M, et al. Maxout networks[J]. JMLR WCP, 2013, 28(3): 1319–1327.

[20] [20] Zhang L L, Lin L, Liang X D, et al. Is faster R-CNN doing well for pedestrian detection [C]//Proceedings of the 14th European Conference on Computer Vision, 2016: 443–457.

[21] [21] Tian Y L, Luo P, Wang X G, et al. Pedestrian detection aided by deep learning semantic tasks[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition, 2015: 5079–5087.

[22] [22] Du X Z, El-Khamy M, Lee J, et al. Fused DNN: a deep neural network fusion approach to fast and robust pedestrian detection[C]//Proceedings of the 2017 IEEE Winter Conference on Applications of Computer Vision, 2017.

[23] [23] Dollár P, Appel R, Belongie S, et al. Fast feature pyramids for object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(8): 1532–1545.

[24] [24] Nam W, Dollár P, Han J H. Local decorrelation for improved pedestrian detection[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems, 2014: 424–432.