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
Speeding up spatio-temporal sliding-window search for efficient event detection in crowded videos
EiMM '09 Proceedings of the 1st ACM international workshop on Events in multimedia
Variable silhouette energy image representations for recognizing human actions
Image and Vision Computing
Volumetric Features for Video Event Detection
International Journal of Computer Vision
Human activity analysis: A review
ACM Computing Surveys (CSUR)
A lattice-based neuro-computing methodology for real-time human action recognition
Information Sciences: an International Journal
Integrating local action elements for action analysis
Computer Vision and Image Understanding
Human action recognition using non-separable oriented 3D dual-tree complex wavelets
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Action recognition using linear dynamic systems
Pattern Recognition
STV-based video feature processing for action recognition
Signal Processing
Exploring trace transform for robust human action recognition
Pattern Recognition
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We propose human action detection based on a successive convex matching scheme. Human actions are represented as sequences of postures and specific actions are detected in video by matching the time-coupled posture sequences to video frames. The template sequence to video registration is formulated as an optimal matching problem. Instead of directly solving the highly non-convex problem, our method convexifies the matching problem into linear programs and refines the matching result by successively shrinking the trust region. The proposed scheme represents the target point space with small sets of basis points and therefore allows efficient searching. This matching scheme is applied to robustly matching a sequence of coupled binary templates simultaneously in a video sequence with cluttered backgrounds.