Human detection based on weighted template matching

This paper proposes a new two-stage human detection method involving matching and verification. A Bayesian framework is developed to verify the matching score obtained from a weighted distance measure. Performance evaluation indicates that the proposed method is able to utilize the flexible matching scheme and produce superior true positive, true negative and low misclassification rates.

[1]  Ramakant Nevatia,et al.  Cluster Boosted Tree Classifier for Multi-View, Multi-Pose Object Detection , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[2]  Adrian Hilton,et al.  A survey of advances in vision-based human motion capture and analysis , 2006, Comput. Vis. Image Underst..

[3]  Larry S. Davis,et al.  Hierarchical Part-Template Matching for Human Detection and Segmentation , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[4]  Bernt Schiele,et al.  Pedestrian detection in crowded scenes , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

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

[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]  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).

[8]  Dariu Gavrila,et al.  A Bayesian, Exemplar-Based Approach to Hierarchical Shape Matching , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Bernt Schiele,et al.  Multi-Aspect Detection of Articulated Objects , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

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

[11]  Ramakant Nevatia,et al.  Detection of multiple, partially occluded humans in a single image by Bayesian combination of edgelet part detectors , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[12]  Chu-Song Chen,et al.  A Cascade of Feed-Forward Classifiers for Fast Pedestrian Detection , 2007, ACCV.