Computer Science ›› 2018, Vol. 45 ›› Issue (6A): 210-214.

• Pattern Recognition & Image Processing • Previous Articles     Next Articles

Pedestrian Detection Based on Objectness and Sapce-Time Covariance Features

LIU Chun-yang,WU Ze-min,HU Lei, LIU Xi   

  1. College of Communications Engineering,PLAUST,Nanjing 210007,China
  • Online:2018-06-20 Published:2018-08-03

Abstract: In order to solve the fusion of space-time information and excessive detection area in pedestrian detection,a pedestrian detection method was proposed based on objectness and space-time covariance features.Firstly,binarized normed gradients algorithm is used for a test image to get objectness evaluations,and a pedestrian detection candidate area is formed.Secondly,the spatial and temporal features are extracted.Finally,a space-time detector based on cova-riance information was proposed to improve the accuracy.Experimental results on the INRIA and Caltech demonstrate that the proposed method outperforms the state-of-art pedestrian detectors in accuracy.

Key words: Computer vision, Covariance features, Objectness, Pedestrian detection

CLC Number: 

  • TP391
[1]IKEUCHI K.Computer Vision:A Reference Guide[M].Sprin- ger Publishing Company,Incorporated,2014.
[3]CAO J,PANG Y,LI X.Pedestrian Detection Inspired by Appearance Constancy and Shape Symmetry[J].IEEE Transactions on Image Processing,2016,25(12):5538-5551.
[4]GIRSHICK R,DONAHUE J,DARRELL T,et al.Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation[C]∥Computer Vision and Pattern Recognition.IEEE,2014:580-587.
[5]HE K,ZHANG X,REN S,et al.Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2014,37(9):1904-1916.
[6]GIRSHICK R.Fast R-CNN[C]∥Proceedings of the 2015 IEEE International Conference on Computer Vision.Santiago,Chile:IEEE,2015:1440-1448.
[7]HE K,ZHANG X,REN S,et al.Deep Residual Learning for Image Recognition[C]∥IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778.
[8]REDMON J,DIVVALA S,GIRSHICK R,et al.You Only Look Once:Unified,Real-Time Object Detection[C]∥Computer Vision and Pattern Recognition.IEEE,2016:779-788.
[9]ANGELOVA A,KRIZHEVSKY A,VANHOUCKE V,et al. Real-Time Pedestrian Detection with Deep Network Cascades[C]∥British Machine Vision Conference.2015:1-32.
[10]ZHANG S,BAUCKHAGE C,CREMERS A B.Informed Haar-Like Features Improve Pedestrian Detection[C]∥IEEEConfe-rence on Computer Vision and Pattern Recognition.2014:947-954.
[11]DOLLAR P,APPEL R,BELONGIE S,et al.Fast Feature Pyramids for Object Detection[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2014,36(8):1532-1545.
[12]ZHANG S,BAUCKHAGE C,CREMERS A B.Informed Haar-Like Features Improve Pedestrian Detection[C]∥IEEE Conference on Computer Vision and Pattern Recognition.IEEE Computer Society,2014:947-954.
[13]NAM W,DOLLAR P,HAN J H.Local Decorrelation For Improved Pedestrian Detection[J].Advances in Neural Information Processing Systems,2014,1:424-432.
[14]PAISITKRIANGKRAI S,SHEN C,HENGEL A V D.Pedes- trian Detection with Spatially Pooled Features and Structured Ensemble Learning[J].IEEE Transactions on Pattern Analysis &Machine Intelligence,2016,38(6):1243.
[15]ZHANG S,BENENSON R,OMRAN M,et al.How Far are We from Solving Pedestrian Detection?[C]∥IEEE Conference on Computer Vision & Pattern Recognition.2016:1259-1267.
[16]ZHANG H,XU M,ZHUO L,et al.A novel optimization framework for salient object detection[J].The Visual Computer,2016,32(1):31-41.
[17]CHENG M M,ZHANG Z,LIN W Y,et al.BING:Binarized Normed Gradients for Objectness Estimation at 300fps[C]∥Computer Vision and Pattern Recognition.IEEE,2014:3286-3293.
[19]BROX T,BREGLER C,MALIK J.Large displacement optical flow[C]∥IEEE Conference on Computer Vision and Pattern Recognition,2009.(CVPR 2009).IEEE,2009:41-48.
[20]DOLLAR P,WOJEK C,SCHIELE B,et al.Pedestrian detec- tion:A benchmark[C]∥IEEE Conference on Computer Vision and Pattern Recognition,2009(CVPR 2009).IEEE,2009:304-311.
[1] ZHANG Ji-kai, LI Qi, WANG Yue-ming, LYU Xiao-qi. Survey of 3D Gesture Tracking Algorithms Based on Monocular RGB Images [J]. Computer Science, 2022, 49(4): 174-187.
[2] TAN Xin-yue, HE Xiao-hai, WANG Zheng-yong, LUO Xiao-dong, QING Lin-bo. Text-to-Image Generation Technology Based on Transformer Cross Attention [J]. Computer Science, 2022, 49(2): 107-115.
[3] GAN Chuang, WU Gui-xing, ZHAN Qing-yuan, WANG Peng-kun, PENG Zhi-lei. Multi-scale Gated Graph Convolutional Network for Skeleton-based Action Recognition [J]. Computer Science, 2022, 49(1): 181-186.
[4] FENG Fu-rong, ZHANG Zhao-gong. Recent Advances for Object Contour Detection Technology [J]. Computer Science, 2021, 48(6A): 1-9.
[5] ZHANG Kai-hua, FAN Jia-qing, LIU Qing-shan. Advances on Visual Object Tracking in Past Decade [J]. Computer Science, 2021, 48(3): 40-49.
[6] LI Ya-ze, LIU Hong-zhe. Object Detection Based on Neighbour Feature Fusion [J]. Computer Science, 2021, 48(12): 264-268.
[7] CHENG Ming, MA Pei, HE Ru-han. Clothing Image Sets Classification Based on Manifold Structure Neural Network [J]. Computer Science, 2021, 48(11A): 391-395.
[8] CHEN Hao-nan, LEI Yin-jie, WANG Hao. Lightweight Lane Detection Model Based on Row-column Decoupled Sampling [J]. Computer Science, 2021, 48(11A): 416-419.
[9] XIE Hai-ping, LI Gao-yuan, YANG Hai-tao, ZHAO Hong-li. Classification Research of Remote Sensing Image Based on Super Resolution Reconstruction [J]. Computer Science, 2021, 48(11A): 424-428.
[10] HE Xin, XU Juan, JIN Ying-ying. Action-related Network:Towards Modeling Complete Changeable Action [J]. Computer Science, 2020, 47(9): 123-128.
[11] LI Ze-wen, LI Zi-ming, FEI Tian-lu, WANG Rui-lin and XIE Zai-peng. Face Image Restoration Based on Residual Generative Adversarial Network [J]. Computer Science, 2020, 47(6A): 230-236.
[12] ZHANG Peng, SONG Yi-fan, ZONG Li-bo, LIU Li-bo. Advances in 3D Object Detection:A Brief Survey [J]. Computer Science, 2020, 47(4): 94-102.
[13] MIAO Yi, ZHAO Zeng-shun, YANG Yu-lu, XU Ning, YANG Hao-ran, SUN Qian. Survey of Image Captioning Methods [J]. Computer Science, 2020, 47(12): 149-160.
[14] LI Huang, WANG Xiao-li, XIANG Xin-guang. Scene Text Detection Based on Triple Segmentation [J]. Computer Science, 2020, 47(11): 142-147.
[15] WANG Xiao-yuan, ZHANG Wen-tao. Overview of Preventing Candid Photos Methods for Electronic Screens [J]. Computer Science, 2019, 46(6A): 41-44.
Full text



No Suggested Reading articles found!