Action Detection in Cluttered Video With Successive Convex Matching

We propose a novel successive convex matching method for human action detection in cluttered video. Human actions are represented as sequences of poses, and specific actions are detected by matching pose sequences. Since we represent actions as the evolution of poses and shapes, the proposed method can detect actions in videos that involve fast camera motions. Template sequence to video registration is nonlinear and highly nonconvex. Instead of directly solving the hard problem, our method convexifies it into a sequence of linear programs and refines the matching by successive trust region shrinkage. The proposed scheme further simplifies the linear programs by representing the target point space with a small set of basis points. The low complexity of the proposed method enables it to search efficiently in a large range. Experiments show that successive convex matching can robustly match a sequence of coupled shape templates simultaneously to target sequences and effectively detect specific actions in cluttered videos.