Mohamed Hussein, Egypt-Japan University of Science and Technology
Human action recognition and human action detection are two closely related problems. In human action recogniton, the purpose is to determine the class of an action performed by a human subject from spatio-temporal measurements of the subject, which are cropped in the time dimension to include only the performed action. On the other hand, in human action detection, the input is not cropped in time and may include multiple action instances, possibly from different classes, and the purpose is to determine the action class and the time period of each action instance in the input sequence. Recent years have witnessed a surge in research efforts on the two problems when the measurements are noisy 3D skeleton data, obtained from cheap consumer-level depth sensors, such as the Microsoft Kinect. In this talk, I will present our efforts in this domain. I will first describe our earlier work in designing fixed-length descriptors for human action recognition from 3D skeleton data. Then, I will introduce a direct deployment of these techniques on human action detection via multi-scale sliding window search, which works in real-time, but, can only process sequences off-line. Then, I will explain our most recent results on real-time online human action detection using a simple linear-time greedy search strategy that we call ‘Efficient Linear Search’, which overcomes the limitations of a more sophisticated dynamic programming strategy in this problem.
