Robust Pedestrian Detection Based on Parallel Channel Cascade Network
HE Jiaojiao1,2, ZHANG Yongping2, YAO Tuozhong2, LIU Ken2,3, XIAO Jiangjian4
1.School of Electronic Control, Chang'an University, Xi'an 710064 2.School of Electronic and Information Engineering, Ningbo University of Techology, Ningbo 315016 3.School of Information Engineering, Chang'an University, Xi'an 710064 4.Advanced Manufacturing Institute Computer Vision Team, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201
Abstract：In the wide-angle field with perspective distortion, the resolution of distant pedestrian is low and there is distortion in a broad range of scales. Aiming at these problems, a robust pedestrian detection algorithm based on parallel channel cascade network is proposed. Firstly, differential information is introduced as weak supervisory information. Secondly, a new feature extraction network, channel cascade network(CCN), is proposed. On this basis, a parallel CCN is designed, and the difference map and the original map are utilized as its input. More abundant image features are fused. Finally, in the region proposal network, the distribution of pedestrians in the picture is characterized by clustering, and anchors meeting the pedestrian's characteristics are clustered. Experimental results show that the proposed algorithm is better than the standard Faster-RCNN algorithm and FPN algorithm for small-scale pedestrian detection in the presence of wide-angle field of view distortion.
[1] 张 强.静态图像上的行人检测方法研究.硕士学位论文.合肥:中国科学技术大学, 2015. (ZHANG Q. Research on Pedestrian Detection Methods on Still Images. Master Dissertation. Hefei, China: University of Science and Technology of China, 2015.) [2] LIN T Y, DOLLÁR P, GIRSHICK R, et al. Feature Pyramid Networks for Object Detection // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2016: 936-944. [3] 王 斌.基于深度学习的行人检测.硕士学位论文.北京:北京交通大学, 2015. (WANG B. Pedestrian Detection Based on Deep Learning. Master Dissertation. Beijing, China: Beijing Jiaotong University, 2015.) [4] DALAL N, TRIGGS B. Histograms of Oriented Gradients for Human Detection // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2005: 886-893. [5] LIENHART R, MAYDT J. An Extended Set of Haar-Like Features for Rapid Object Detection // Proc of the International Conference on Image Processing. Washington, USA: IEEE, 2002: I-900-I-903. [6] MU Y D, YAN S C, LIU Y, et al. Discriminative Local Binary Patterns for Human Detection in Personal Album // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2008. DOI: 10.1109/CVPR.2008.4587800. [7] CHEN P H, LIN C J, SCHÖLKOPF B. A Tutorial on V-Support Vector Machines. Applied Stochastic Models in Business and Industry, 2005, 21(2): 111-136. [8] KUO W C, HARIHARAN B, MALIK J. DeepBox: Learning Objectness with Convolutional Networks // Proc of the IEEE International Conference on Computer Vision. Washington, USA: IEEE, 2015: 2479-2487. [9] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2014: 580-587. [10] HE K M, ZHANG X Y, REN S Q, et al. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1904-1916. [11] GIRSHICK R. Fast R-CNN[J/OL]. [2018-07-23]. https://arxiv.org/pdf/1504.08083.pdf. [12] REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149. [13] HE K M, ZHANG X Y, REN S Q, et al. Deep Residual Learning for Image Recognition // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2015: 770-778. [14] KONG T, YAO A B, CHEN Y R, et al. HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2016: 845-853. [15] LIU W, ANGUELOV D, ERHAN D, et al. SSD: Single Shot MultiBox Detector // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2016: 21-37. [16] CAI Z W, FAN Q F, FERIS R S, et al. A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection // Proc of the European Conference on Computer Vision. Berlin, Germany: Springer, 2016: 354-370. [17] HARIHARAN B, ARBELÁEZ P, GIRSHICK R, et al. Hypercolumns for Object Segmentation and Fine-Grained Localization // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2015: 447-456. [18] LONG J, SHELHAMER E, DARRELL T. Fully Convolutional Networks for Semantic Segmentation // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2015: 3431-3440. [19] HE K M, GKIOXARI G, DOLLÁR P, et al. Mask R-CNN[J/OL]. [2018-07-23]. https://arxiv.org/pdf/1703.06870.pdf. [20] ZHANG Z S, QIAO S Y, XIE C, et al. Single-Shot Object Detection with Enriched Semantics[J/OL]. [2018-07-23]. https://arxiv.org/pdf/1712.00433.pdf. [21] CAI Z W, VASCONCELOS N. Cascade R-CNN: Delving into High Quality Object Detection[C/OL]. [2018-07-23]. https://arxiv.org/pdf/1712.00726.pdf. [22] CHEN Y L, WANG Z C, PENG Y X, et al. Cascaded Pyramid Network for Multi-person Pose Estimation[C/OL]. [2018-07-23]. https://arxiv.org/pdf/1711.07319.pdf. [23] LIN T Y, DOLLÁR P, GIRSHICK R, et al. Feature Pyramid Networks for Object Detection[C/OL]. [2018-07-23]. https://arxiv.org/pdf/1612.03144.pdf. [24] SIMONYAN K, ZISSERMAN A. Very Deep Convolutional Networks for Large-Scale Image Recognition[C/OL]. [2018-07-23]. https://arxiv.org/pdf/1409.1556v6.pdf. [25] REDMON J, FARHADI A. YOLO9000: Better, Faster, Stronger // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2016: 6517-6525. [26] REDMON J, FARHADI A. YOLOv3: An Incremental Improvement[C/OL]. [2018-07-23]. https://arxiv.org/pdf/1804.02767.pdf.
2018, Vol. 31 
