Recognition of Visual Activities and Interactions by Stochastic Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Beyond Tracking: Modelling Activity and Understanding Behaviour
International Journal of Computer Vision
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Statistical Analysis of Dynamic Actions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Free viewpoint action recognition using motion history volumes
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Detecting abnormal human behaviour using multiple cameras
Signal Processing
Robust Sequential Data Modeling Using an Outlier Tolerant Hidden Markov Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Audio-visual speech modeling for continuous speech recognition
IEEE Transactions on Multimedia
Audio–Visual Affective Expression Recognition Through Multistream Fused HMM
IEEE Transactions on Multimedia
Automatic video-based human motion analyzer for consumer surveillance system
IEEE Transactions on Consumer Electronics
Bayesian filter based behavior recognition in workflows allowing for user feedback
Computer Vision and Image Understanding
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Monitoring real world environments such as industrial scenes is a challenging task due to heavy occlusions, resemblance of different processes, frequent illumination changes, etc. We propose a robust framework for recognizing workflows in such complex environments, boasting a threefold contribution: Firstly, we employ a novel holistic scene descriptor to efficiently and robustly model complex scenes, thus bypassing the very challenging tasks of target recognition and tracking. Secondly, we handle the problem of limited visibility and occlusions by exploiting redundancies through the use of merged information from multiple cameras. Finally, we use the multivariate Student-t distribution as the observation likelihood of the employed Hidden Markov Models, in order to further enhance robustness.We evaluate the performance of the examined approaches under real-life visual behavior understanding scenarios and we compare and discuss the obtained results.