Disparity Statistics for Pedestrian Detection: Combining Appearance, Motion and Stereo

Pedestrian detection is an important problem in computer vision due to its importance for applications such as visual surveillance, robotics, and automotive safety. This paper pushes the state-of-the-art of pedestrian detection in two ways. First, we propose a simple yet highly effective novel feature based on binocular disparity, outperforming previously proposed stereo features. Second, we show that the combination of different classifiers often improves performance even when classifiers are based on the same feature or feature combination. These two extensions result in significantly improved performance over the state-of-the-art on two challenging datasets.

[1]  Robert P. W. Duin,et al.  Experiments with Classifier Combining Rules , 2000, Multiple Classifier Systems.

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

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

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

[5]  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).

[6]  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).

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

[8]  Navneet Dalal,et al.  Finding People in Images and Videos , 2006 .

[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.

[10]  Dariu Gavrila,et al.  Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle , 2007, International Journal of Computer Vision.

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

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

[13]  Luc Van Gool,et al.  A mobile vision system for robust multi-person tracking , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  S. Munder,et al.  Pedestrian recognition using combined low-resolution depth and intensity images , 2008, 2008 IEEE Intelligent Vehicles Symposium.

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

[16]  Serge J. Belongie,et al.  Simultaneous Learning and Alignment: Multi-Instance and Multi-Pose Learning ? , 2008 .

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

[18]  Roberto Cipolla,et al.  MCBoost: Multiple Classifier Boosting for Perceptual Co-clustering of Images and Visual Features , 2008, NIPS.

[19]  Dariu Gavrila,et al.  High-Level Fusion of Depth and Intensity for Pedestrian Classification , 2009, DAGM-Symposium.

[20]  Luc Van Gool,et al.  Robust Multiperson Tracking from a Mobile Platform , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[22]  Dariu Gavrila,et al.  Monocular Pedestrian Detection: Survey and Experiments , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Daniel Cremers,et al.  Anisotropic Huber-L1 Optical Flow , 2009, BMVC.

[24]  Bernt Schiele,et al.  Multi-cue onboard pedestrian detection , 2009, CVPR.

[25]  Marc Niethammer,et al.  Continuous maximal flows and Wulff shapes: Application to MRFs , 2009, CVPR.

[26]  Sebastian Nowozin,et al.  On feature combination for multiclass object classification , 2009, 2009 IEEE 12th International Conference on Computer Vision.

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

[28]  Bernt Schiele,et al.  Pictorial structures revisited: People detection and articulated pose estimation , 2009, CVPR.

[29]  Luc Van Gool,et al.  Moving obstacle detection in highly dynamic scenes , 2009, 2009 IEEE International Conference on Robotics and Automation.