Active shape models—their training and application
Computer Vision and Image Understanding
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Shape Priors for Level Set Representations
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Geometric Level Set Methods in Imaging,Vision,and Graphics
Geometric Level Set Methods in Imaging,Vision,and Graphics
Detection of People Carrying Objects: A Motion-Based Recognition Approach
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inferring 3D body pose from silhouettes using activity manifold learning
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Dynamic shape outlier detection for human locomotion
Computer Vision and Image Understanding
Applications of a simple characterization of human gait in surveillance
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
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In this paper, we introduce a framework for carrying object detection in different people from different views using pose preserving dynamic shape models. We model dynamic shape deformations in different people using kinematics manifold embedding and decomposable generative models by kernel map and multilinear analysis. The generative model supports pose-preserving shape reconstruction in different people, views and body poses. Iterative estimation of shape style and view with pose preserving generative model allows estimation of outlier in addition to accurate body pose. The model is also used for hole filling in the background-subtracted silhouettes using mask generated from the best fitting shape model. Experimental results show accurate estimation of carrying objects with hole filling in discrete and continuous view variations