A Combined Bayesian Markovian Approach for Behaviour Recognition
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Supplementing Markov Chains with Additional Features for Behavioural Analysis
AVSS '06 Proceedings of the IEEE International Conference on Video and Signal Based Surveillance
Evaluation of Motion Segmentation Quality for Aircraft Activity Surveillance
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Video understanding for complex activity recognition
Machine Vision and Applications
Automatic video interpretation: a novel algorithm for temporal scenario recognition
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
A multiview approach to tracking people in crowded scenes using a planar homography constraint
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
A hierarchical approach for visual suspicious behavior detection in aircrafts
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
COST'10 Proceedings of the 2010 international conference on Analysis of Verbal and Nonverbal Communication and Enactment
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Under the framework of the European Union Funded SAFEE project, this paper gives an overview of a novel monitoring and scene analysis system developed for use onboard aircraft in spatially constrained environments. The techniques discussed herein aim to warn on-board crew about pre-determined indicators of threat intent (such as running or shouting in the cabin), as elicited from industry and security experts. The subject matter experts believe that activities such as these are strong indicators of the beginnings of undesirable chains of events or scenarios, which should not be allowed to develop aboard aircraft. This project aimes to detect these scenarios and provide advice to the crew. These events may involve unruly passengers or be indicative of the precursors to terrorist threats. With a state of the art tracking system using homography intersections of motion images, and probability based Petri nets for scene understanding, the SAFEE behavioural analysis system automatically assesses the output from multiple intelligent sensors, and creates recommendations that are presented to the crew using an integrated airborn user interface. Evaluation of the system is conducted within a full size aircraft mockup, and experimental results are presented, showing that the SAFEE system is well suited to monitoring people in confined environments, and that meaningful and instructive output regarding human actions can be derived from the sensor network within the cabin.