Example-Based Object Detection in Images by Components
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Pedestrians Using Patterns of Motion and Appearance
International Journal of Computer Vision
Pedestrian Detection in Crowded Scenes
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Contour-Based Learning for Object Detection
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'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 Bayesian, Exemplar-Based Approach to Hierarchical Shape Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Groups of Adjacent Contour Segments for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation and Tracking of Multiple Humans in Crowded Environments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple Component Learning for Object Detection
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Monocular Pedestrian Detection: Survey and Experiments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape-Based Human Detection and Segmentation via Hierarchical Part-Template Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
An improved template matching method for object detection
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Pedestrian Detection: An Evaluation of the State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Detecting multiple humans in crowded scenes is challenging because the humans are often partially or even totally occluded by each other. In this paper, we propose a novel algorithm for partial inter-occlusion reasoning in human detection based on variational mean field theory. The proposed algorithm can be integrated with various part-based human detectors using different types of features, object representations, and classifiers. The algorithm takes as the input an initial set of possible human objects (hypotheses) detected using a part-based human detector. Each hypothesis is decomposed into a number of parts and the occlusion status of each part is inferred by the proposed algorithm. Specifically, initial detections (hypotheses) with spatial layout information are represented in a graphical model and the inference is formulated as an estimation of the marginal probability of the observed data in a Bayesian network. The variational mean field theory is employed as an effective estimation technique. The proposed method was evaluated on popular datasets including CAVIAR, iLIDS, and INRIA. Experimental results have shown that the proposed algorithm is not only able to detect humans under severe occlusion but also enhance the detection performance when there is no occlusion.