A Multi-object Motion-tracking Method for Video Surveillance
SNPD '07 Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 01
Automatic Classification of Abandoned Objects for Surveillance of Public Premises
CISP '08 Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 4 - Volume 04
A survey on behavior analysis in video surveillance for homeland security applications
AIPR '08 Proceedings of the 2008 37th IEEE Applied Imagery Pattern Recognition Workshop
Carried object detection using ratio histogram and its application to suspicious event analysis
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Human activity recognition based on the blob features
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Robust object tracking using correspondence voting for smart surveillance visual sensing nodes
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Recognition of Human Activities: A Survey
IEEE Transactions on Circuits and Systems for Video Technology
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ideo surveillance has gained importance in law enforcement, security and military applications. The system consists of processing steps such as object detection, movement tracking, and activity monitoring. The paper contribution is to present the human activity analysis system that both detect a human with carrying or abandoning an object and segments the object from the human so that it can be tracked. Segmentation of objects is done from the background using advance Gaussian mixture model. The tracking algorithm considers the human as whole from frame to frame, it does not track the human parts such as limbs. Object features such as center of mass, size, and bounding box are used in this paper to estimate a matching between objects in consecutive frames. As the object is segmented and tracked, Bayesian inference framework is used for event analysis. This system uses a single camera view and unusual activity is detected using the detected objects and object tracking result. The operator is notified if an unusual activity is detected.