Pedestrian detection with dynamic iterative bootstrapping

Recent years have seen the increasing importance of pedestrian detection, which is a key problem in computer vision. In this paper, we propose a novel pedestrian detection approach based on Faster R-CNN. In order to obtain high-quality candidate regions, relevant adjustments with more precise anchors are made for region proposal network. To resolve the data imbalance issue in the classifier training, we propose a dynamic iterative bootstrapping method where the hard negative examples are automatically selected and the weights of the network are updated iteratively by them to make the training more effective. The square method is used to optimize the multi-task loss in our approach, which can accelerate convergence and reduce sensitivity. Experimental results on different widely used benchmark datasets show that the proposed approach achieves better performance in comparison with other common methods.

[1]  Joon Hee Han,et al.  Local Decorrelation For Improved Pedestrian Detection , 2014, NIPS.

[2]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Fatih Murat Porikli,et al.  Pedestrian Detection via Classification on Riemannian Manifolds , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Pietro Perona,et al.  The Fastest Pedestrian Detector in the West , 2010, BMVC.

[6]  Abhinav Gupta,et al.  Training Region-Based Object Detectors with Online Hard Example Mining , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Bernt Schiele,et al.  Filtered channel features for pedestrian detection , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Luc Van Gool,et al.  Handling Occlusions with Franken-Classifiers , 2013, 2013 IEEE International Conference on Computer Vision.

[9]  Xiaogang Wang,et al.  Deep Learning Strong Parts for Pedestrian Detection , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[10]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[11]  Kah Kay Sung,et al.  Learning and example selection for object and pattern detection , 1995 .

[12]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[13]  Bin Yang,et al.  Convolutional Channel Features , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[14]  Bernt Schiele,et al.  Ten Years of Pedestrian Detection, What Have We Learned? , 2014, ECCV Workshops.

[15]  Bin Yang,et al.  Aggregate channel features for multi-view face detection , 2014, IEEE International Joint Conference on Biometrics.

[16]  Koen E. A. van de Sande,et al.  Selective Search for Object Recognition , 2013, International Journal of Computer Vision.

[17]  Abhinav Gupta,et al.  Unsupervised Learning of Visual Representations Using Videos , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[18]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Frank Hutter,et al.  Online Batch Selection for Faster Training of Neural Networks , 2015, ArXiv.

[20]  Xiaogang Wang,et al.  Pedestrian detection aided by deep learning semantic tasks , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[22]  Pietro Perona,et al.  Pedestrian Detection: An Evaluation of the State of the Art , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[24]  Yann LeCun,et al.  Pedestrian Detection with Unsupervised Multi-stage Feature Learning , 2012, 2013 IEEE Conference on Computer Vision and Pattern Recognition.