Recognition of Visual Activities and Interactions by Stochastic Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Temporal Pattern Generation Using Hidden Markov Model Based Unsupervised Classification
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
Beyond Tracking: Modelling Activity and Understanding Behaviour
International Journal of Computer Vision
Two phases of v1 activity for visual recognition of natural images
Journal of Cognitive Neuroscience
Robust Human Behavior Modeling from Multiple Cameras
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Behavior recognition from multiple views using fused hidden markov models
SETN'10 Proceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Generalized nonlinear relevance feedback for interactive content-based retrieval and organization
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Neural Networks
Bayesian filter based behavior recognition in workflows allowing for user feedback
Computer Vision and Image Understanding
Behavior recognition from video based on human constrained descriptor and adaptable neural networks
Proceedings of the 4th ACM/IEEE international workshop on Analysis and retrieval of tracked events and motion in imagery stream
A top-down event-driven approach for concurrent activity recognition
Multimedia Tools and Applications
Robust human action recognition scheme based on high-level feature fusion
Multimedia Tools and Applications
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Human behavior recognition and real world environments monitoring constitute challenging research problems rapidly gaining momentum over the last years. Methods for time series classification like the Hidden Markov Models have been employed in the past for similar tasks, however in many challenging cases they fail, since some behaviors are much more difficult to model than others. This happens particularly in cases that there is scarcity of labelled data. In this paper we introduce a novel re-adjustment framework of behavior recognition and classification by allowing the user incorporation in the learning process. The proposed Evaluative Rectification approach aims at dynamically correcting erroneous classification results to enhance the behavior modeling and therefore the overall classification rates. We evaluate the performance of the examined approach in a challenging real-life industrial environment of an automobile manufacturer. Our experiments indicate a significant outperformance of the proposed Evaluative Rectification scheme compared with traditional classification frameworks, such as Hidden Markov Models.