Local Haar-like features in edge maps for pedestrian detection

Pedestrian detection is a significant problem in computer vision. Dollar et al. at [1] indicate that current methods are not efficient enough to solve the problem and it remains challenging. Besides, most of the proposed features are also not efficient in describing the features of pedestrians. In this paper, we concentrate on this issue and introduce the local Haar-like features in edge maps to describe the edge/contour of pedestrians efficiently. To reduce the background noise, localized normalization is proposed. Also the integral and tilted integral images are applied to fast extraction of the feature values. The experiments on pedestrian detection show both the describing and computational efficiency of the proposed features.

[1]  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).

[2]  Pietro Perona,et al.  Pedestrian detection: A benchmark , 2009, CVPR.

[3]  Rainer Lienhart,et al.  An extended set of Haar-like features for rapid object detection , 2002, Proceedings. International Conference on Image Processing.

[4]  E. Rückert.  Detecting Pedestrians by Learning Shapelet Features , 2007 .

[5]  Mei-Chen Yeh,et al.  Fast Human Detection Using a Cascade of Histograms of Oriented Gradients , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[6]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[7]  Tomaso A. Poggio,et al.  A Trainable System for Object Detection , 2000, International Journal of Computer Vision.

[8]  Wei Huang,et al.  Detection and tracking of multiple moving objects in video , 2007, VISAPP.

[9]  Ramakant Nevatia,et al.  Detection and Tracking of Multiple, Partially Occluded Humans by Bayesian Combination of Edgelet based Part Detectors , 2007, International Journal of Computer Vision.