Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to Parse Pictures of People
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Contour-Based Object Tracking with Occlusion Handling in Video Acquired Using Mobile Cameras
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Pedestrians Using Patterns of Motion and Appearance
International Journal of Computer Vision
Strike a Pose: Tracking People by Finding Stylized Poses
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 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
Closely Coupled Object Detection and Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
A Field Model for Human Detection and Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo- and neural network-based pedestrian detection
IEEE Transactions on Intelligent Transportation Systems
An efficient approach to detecting pedestrians in video
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Tracking in a Dense Crowd Using Multiple Cameras
International Journal of Computer Vision
Advances in view-invariant human motion analysis: a review
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Action recognition via bio-inspired features: The richness of center-surround interaction
Computer Vision and Image Understanding
Scene Aware Detection and Block Assignment Tracking in crowded scenes
Image and Vision Computing
Accurate pedestrian counting system based on local features
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
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We describe an approach for detecting and segmenting humans with extensive posture articulations in crowded video sequences. In our method we learn a set of mean posture clusters, and a codebook of local shape distributions for humans in various postures. Detection proceeds in two stages: first instances of the codebook entries cast votes for locations of humans in the video and their respective postures. Subsequently, consistent hypotheses are found as maxima within a voting space. The segmentation of humans in the scene is initialized by the corresponding posture clusters and contours are evolved to obtain precise and consistent segmentations. Our experimental results indicate that the framework provides a simple yet effective means for aggregating local and global shape-based cues. The proposed method is capable of detecting and segmenting humans in crowded scenes as they perform a diverse set of activities and undergo a wide range of articulations within different contexts.