Boosted string representation and its application to video surveillance
Pattern Recognition
Search strategies for shape regularized active contour
Computer Vision and Image Understanding
International Journal of Computer Vision
Bottom-up recognition and parsing of the human body
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Hierarchical vibrations for part-based recognition of complex objects
Pattern Recognition
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
Efficient inference with multiple heterogeneous part detectors for human pose estimation
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
International Journal of Computer Vision
Skeleton Search: Category-Specific Object Recognition and Segmentation Using a Skeletal Shape Model
International Journal of Computer Vision
Detection human motion with heel strikes for surveillance analysis
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Discriminative Appearance Models for Pictorial Structures
International Journal of Computer Vision
Heel strike detection based on human walking movement for surveillance analysis
Pattern Recognition Letters
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We present a 3-level hierarchical model for localizing human bodies in still images from arbitrary viewpoints. We first fit a simple tree-structured model defined on a small landmark set along the body contours by Dynamic Programming (DP). The output is a series of proposal maps that encode the probabilities of partial body configurations. Next, we fit a mixture of view-dependent models by Sequential Monte Carlo (SMC), which handles self-occlusion, anthropometric constraints, and large viewpoint changes. DP and SMC are designed to search in opposite directions such that the DP proposals are utilized effectively to initialize and guide the SMC inference. This hybrid strategy of combining deterministic and stochastic search ensures both the robustness and efficiency of DP, and the accuracy of SMC. Finally, we fit an expanded mixture model with increased landmark density through local optimization. The model hierarchy is trained on a large number of gait images. Extensive tests on cluttered images with varying poses including walking, dancing and various types of sports activities demonstrate the feasibility of the proposed approach.