Real-Time Human Detection Based on Optimized Integrated Channel Features

We propose an optimized integrated channel features which can effectively improve the detection performance at the frame rate of 30 fps on images size of 640x480. The proposed method utilizes the distribution of filter response from positive and negative features to formulate the optimized combination of multiple filters. The optimized combination coefficient is learned from linear discriminative criterion which is superior to integrated channel features with random coefficients. Experimental results based on INRIA dataset shows the superiority of our method to other state-of-arts methods.

[1]  Chu-Song Chen,et al.  Fast Human Detection Using a Novel Boosted Cascading Structure With Meta Stages , 2008, IEEE Transactions on Image Processing.

[2]  Andrew J. Davison,et al.  Active Matching , 2008, ECCV.

[3]  Larry S. Davis,et al.  Human detection using partial least squares analysis , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[4]  Pietro Perona,et al.  Integral Channel Features , 2009, BMVC.

[5]  Shuicheng Yan,et al.  An HOG-LBP human detector with partial occlusion handling , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[6]  Greg Mori,et al.  Detecting Pedestrians by Learning Shapelet Features , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  David A. McAllester,et al.  A discriminatively trained, multiscale, deformable part model , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Subhransu Maji,et al.  Classification using intersection kernel support vector machines is efficient , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Larry S. Davis,et al.  A Pose-Invariant Descriptor for Human Detection and Segmentation , 2008, ECCV.

[10]  Bernt Schiele,et al.  New features and insights for pedestrian detection , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  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).

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

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