Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Fast Human Detection Using a Cascade of Histograms of Oriented Gradients
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A Pose-Invariant Descriptor for Human Detection and Segmentation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Moving obstacle detection in highly dynamic scenes
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Proceedings of the Workshop on Use of Context in Vision Processing
Proceedings of the Workshop on Use of Context in Vision Processing
A robust and scalable approach to face identification
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Journal on Image and Video Processing - Special issue on advanced video-based surveillance
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Appearing as an important task in computer vision, pedestrian detection has been widely investigated in the recent years. To design a robust detector, we propose a feature descriptor called Local Response Context (LRC). This descriptor captures discriminative information regarding the surrounding of the person's location by sampling the response map obtained by a generic sliding window detector. A partial least squares regression model using LRC descriptors is learned and employed as a second classification stage (after the execution of the generic detector to obtain the response map). Experiments based on the ETHZ pedestrian dataset show that the proposed approach improves significantly the results achieved by the generic detector alone and is comparable to the state-of-the-art methods.