Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of Visual Activities and Interactions by Stochastic Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-camera spatio-temporal fusion and biased sequence-data learning for security surveillance
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Summarising contextual activity and detecting unusual inactivity in a supportive home environment
Pattern Analysis & Applications
Detecting Irregularities in Images and in Video
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Learning and inferring transportation routines
Artificial Intelligence
Policy recognition in the abstract hidden Markov model
Journal of Artificial Intelligence Research
Where is . •.? learning and utilizing motion patterns of persons with mobile robots
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
An on-line time warping algorithm for tracking musical performances
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Detecting unusual activity in video
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Learning semantic scene models from observing activity in visual surveillance
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Goal-based trajectory analysis for unusual behaviour detection in intelligent surveillance
Image and Vision Computing
Human activity monitoring by local and global finite state machines
Expert Systems with Applications: An International Journal
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We propose a new method that treats visible human behaviour at the level of navigational strategies. By inferring intentions in terms of known goals, it is possible to explain the behaviour of people moving around within the field of view of a video camera. The approach presented here incorporates models of navigation from within psychology which are both simple and conceptually plausible, whilst providing good results in an event-detection application. The output is in the form of statements involving goals, such as ''Agent 25 went to exit 8 via sub-goals 34 and 21'' for a given navigational strategy, an image representing the path through the scene, and an overall score for each trajectory. The central algorithm generates all plausible paths through the scene to known goal sites and then compares each path to the agent's actual trajectory thus finding the most likely explanation for their behaviour. Two navigational strategies are examined, shortest path and simplest path. Experimental results are presented for an outdoor car-park and an indoor foyer scene, and our method is found to produce psychologically plausible explanations in the majority of cases. We propose a novel approach to determining the effectiveness of event detection systems, and evaluate the method presented here against this new ground truth. This evaluation method uses human observers to judge the behaviour shown in various video clips, then uses these judgements in correlation with those of the software. We compare the method with a standard machine learning approach based on nearest neighbour. Finally we consider the application of such a system in a binary event-detection or behaviour filtering system.