Leveraging temporal, contextual and ordering constraints for recognizing complex activities in video
- 1 June 2007
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 10636919,p. 1-8
Abstract
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, object-oriented 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, this study proposes the Erlang distribution as a comprehensive model of idle time between actions and frequency of observing new actions. We show results for our approach on real video sequences containing complex activities.Keywords
This publication has 13 references indexed in Scilit:
- Activity Recognition of Assembly Tasks Using Body-Worn Microphones and AccelerometersIEEE Transactions on Pattern Analysis and Machine Intelligence, 2006
- Recognition of Composite Human Activities through Context-Free Grammar Based Representation2006
- Joint Recognition of Complex Events and Track Matching2006
- Automatic Discovery of Action Taxonomies from Multiple Views2006
- Actions as space-time shapes2005
- Layered representations for human activity recognition2003
- Recognizing action at a distance2003
- Human action detection using PNF propagation of temporal constraints2002
- The recognition of human movement using temporal templatesIEEE Transactions on Pattern Analysis and Machine Intelligence, 2001
- Exploiting human actions and object context for recognition tasks1999