Fast Human Detection by Boosting Histograms of Oriented Gradients

In this paper, a novel real-time human detection system based on Viola's face detection framework and Histograms of Oriented Gradients (HOG) features is presented. Each bin of the histogram is treated as a feature and used as the basic building element of the cascade classifier. The system keeps both the discriminative power of HOG features for human detection and the real-time property of Viola's face detection framework. Experiments on Daimler Chrysler pedestrian benchmark data set and INRIA human database demonstrate that this framework is more powerful than Viola's object detection framework on human detection.

[1]  Cordelia Schmid,et al.  Human Detection Based on a Probabilistic Assembly of Robust Part Detectors , 2004, ECCV.

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

[3]  Hugues Hoppe,et al.  Progressive meshes , 1996, SIGGRAPH.

[4]  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).

[5]  David Levin,et al.  Surface simplification using a discrete curvature norm , 2002, Comput. Graph..

[6]  Pan Zhigeng,et al.  A new mesh simplification algorithm based on triangle collapses , 2001 .

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

[8]  A. Shashua,et al.  Pedestrian detection for driving assistance systems: single-frame classification and system level performance , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[9]  E LorensenWilliam,et al.  Decimation of triangle meshes , 1992 .

[10]  Dariu Gavrila,et al.  Real-time object detection for "smart" vehicles , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[11]  Cordelia Schmid,et al.  Human Detection Using Oriented Histograms of Flow and Appearance , 2006, ECCV.

[12]  Paul A. Viola,et al.  Detecting Pedestrians Using Patterns of Motion and Appearance , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[13]  Denis Laurendeau,et al.  Multiresolution Surface Modeling Based on Hierarchical Triangulation , 1996, Comput. Vis. Image Underst..

[14]  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).

[15]  Dariu Gavrila,et al.  An Experimental Study on Pedestrian Classification , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Issac J. Trotts,et al.  Smooth hierarchical surface triangulations , 1997 .

[17]  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.

[18]  Bernd Hamann,et al.  A data reduction scheme for triangulated surfaces , 1994, Comput. Aided Geom. Des..

[19]  Yair Weiss,et al.  Learning object detection from a small number of examples: the importance of good features , 2004, CVPR 2004.

[20]  Tomaso A. Poggio,et al.  Example-Based Object Detection in Images by Components , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Dinesh Manocha,et al.  Simplification envelopes , 1996, SIGGRAPH.

[22]  Tomaso A. Poggio,et al.  Pedestrian detection using wavelet templates , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.