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, Pedestrian detection, Objectness, Covariance features

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.
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