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
Pedestrian detection in uncontrolled environments using stereo and biometric information
Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks
People detection and tracking using stereo vision and color
Image and Vision Computing
Robust pedestrian detection and tracking in crowded scenes
Image and Vision Computing
Human detection with a multi-sensors stereovision system
ICISP'10 Proceedings of the 4th international conference on Image and signal processing
Stereo- and neural network-based pedestrian detection
IEEE Transactions on Intelligent Transportation Systems
A Framework for Evaluating Stereo-Based Pedestrian Detection Techniques
IEEE Transactions on Circuits and Systems for Video Technology
Collecting pedestrian trajectories
Neurocomputing
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People counting and human detection have always been important objectives in visual surveillance. With the decrease in the cost of stereo cameras, they can potentially be used to develop new algorithms and achieve better accuracy. This paper introduces a multi-cue-based method for individual person segmentation in stereo vision. Shape cues inside the crowd are explored with a block-based Implicit Shape Model. Depth cues are obtained from the disparity values of some foreground blobs, which are calculated concurrently during crowd segmentation. Crowd segmentation is therefore achieved with evidences from both shape and depth cues. The methods were evaluated on two video sequences. The results show that the segmentation performance has been improved when depth cues are considered.