3D Shape Context and Distance Transform for action recognition

We propose the use of 3D (2D+time) Shape Context to recognize the spatial and temporal details inherent in human actions. We represent an action in a video sequence by a 3D point cloud extracted by sampling 2D silhouettes over time. A non-uniform sampling method is introduced that gives preference to fast moving body parts using a Euclidean 3D Distance Transform. Actions are then classified by matching the extracted point clouds. Our proposed approach is based on a global matching and does not require specific training to learn the model. We test the approach thoroughly on two publicly available datasets and compare to several state-ofthe-art methods. The achieved classification accuracy is on par with or superior to the best results reported to date.

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Franziska Meier, Irfan A. Essa, Matthias Grundmann
• Keywords: 3D Point Cloud, Computer Vision, Extracted Point Clouds, ICPR 2008, Non-uniform Sampling Method