2010 · 1.4K citations · 19 references
ComputingComputer ScienceComputer VisionHuman-robot InteractionRobot VisionAction RecognitionDepth EstimationPoint Cloud ProcessingPose EstimationPostureActivity RecognitionGraph-based Action RecognitionGraph-based Action RepresentationRecognition Errors
The paper proposes a method to recognize human actions from depth map sequences. It models action dynamics with an action graph and represents salient postures as a bag of 3D points sampled via a simple projection‑based scheme from depth maps. Experiments show over 90 % accuracy using only about 1 % of 3D points, halving errors compared to 2D silhouette methods, and demonstrate robustness to occlusion.
This paper presents a method to recognize human actions from sequences of depth maps. Specifically, we employ an action graph to model explicitly the dynamics of the actions and a bag of 3D points to characterize a set of salient postures that correspond to the nodes in the action graph. In addition, we propose a simple, but effective projection based sampling scheme to sample the bag of 3D points from the depth maps. Experimental results have shown that over 90% recognition accuracy were achieved by sampling only about 1% 3D points from the depth maps. Compared to the 2D silhouette based recognition, the recognition errors were halved. In addition, we demonstrate the potential of the bag of points posture model to deal with occlusions through simulation.
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Visual pattern recognition by moment invariants
Ming-Kuei Hu · IRE Transactions on Information Theory · 1962 · 7.5K citations