Real-Time exact graph matching with application in human action recognition
HBU'12 Proceedings of the Third international conference on Human Behavior Understanding
3D teleimmersive activity classification based on application-system metadata
Proceedings of the 21st ACM international conference on Multimedia
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We propose an efficient approach that unifies activity categorization with space-time localization. The main idea is to pose activity detection as a maximum-weight connected subgraph problem over a learned space-time graph constructed on the test sequence. We show this permits an efficient branch-and-cut solution for the best-scoring — and possibly non-cubically shaped — portion of the video for a given activity classifier. The upshot is a fast method that can evaluate a broader space of candidates than was previously practical, which we find often leads to more accurate detection. We demonstrate the proposed algorithm on three datasets, and show its speed and accuracy advantages over multiple existing search strategies.