Sparse B-spline polynomial descriptors for human activity recognition
Image and Vision Computing
A survey on vision-based human action recognition
Image and Vision Computing
Representing pairwise spatial and temporal relations for action recognition
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Action recognition based on human movement characteristics
WMVC'09 Proceedings of the 2009 international conference on Motion and video computing
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We propose a novel method for action detection based on a new action descriptor called a shape flow that represents both the shape and movement of an object in a holistic and parsimonious manner. We find actions by finding shape flows in a target video that are similar to a template shape flow. Shape flows are largely independent of appearance, and the match cost function that we propose is invariant to scale changes and smooth nonlinear deformation in space and time. The problem of matching shape flows is difficult, however, yielding a large, non-convex, integer program. We propose a novel relaxation method based on successive convexification that converts this hard program into a vastly smaller linear program: By using only those variables that appear on the 4D lower convex hull of the matching cost volume, most of the variables in the linear program may be eliminated. Experiments confirm that the proposed shape flow method can successfully detect complex actions in cluttered video, even with self-occlusion, camera motion, and intra-class variation.