Leveraging temporal, contextual and ordering constraints for recognizing complex activities in video

We present a scalable approach to recognizing and describing complex activities in video sequences. We are interested in long-term, sequential activities that may have several parallel streams of action. Our approach integrates temporal, contextual and ordering constraints with output from low-level visual detectors to recognize complex, long-term activities. We argue that a hierarchical, objectoriented design lends our solution to be scalable in that higher-level reasoning components are independent from the particular low-level detector implementation and that recognition of additional activities and actions can easily be added. Three major components to realize this design are: a dynamic Bayesian network structure for representing activities comprised of partially ordered sub-actions, an object-oriented action hierarchy for building arbitrarily complex action detectors and an approximate Viterbi-like algorithm for inferring the most likely observed sequence of actions. Additionally...

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Benjamin Laxton, Jongwoo Lim, David J. Kriegman
• Keywords: Complex Action Detectors, Complex Activities, Computer Vision, CVPR 2007, Long-term Activities, Low-level Visual Detectors, Particular Low-level Detector