From image sequences towards conceptual descriptions
Image and Vision Computing
Visual surveillance in a dynamic and uncertain world
Artificial Intelligence - Special volume on computer vision
Recognition of Visual Activities and Interactions by Stochastic Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking Human Motion Using Multiple Cameras
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Detecting Moving Shadows: Algorithms and Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection and Explanation of Anomalous Activities: Representing Activities as Bags of Event n-Grams
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Detecting stochastically scheduled activities in video
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Automatic video interpretation: a novel algorithm for temporal scenario recognition
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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Automatically detecting suspicious human activities in restricted environments such as airports, parking lots and banks represents an open issue for the last generation surveillance systems. In this paper, we present an approach that allows to detect anomalies in a video sequence without any need of describing a priori "abnormal" activities. In particular, we first introduce a normal activities model based on the concept of elementary actions observable by means of image understanding procedures. We then provide an algorithm based on the use of decision trees that can quickly detect an abnormal situation as variation of currently processed activity with respect to normal patterns contained in the system knowledge base. Our preliminary experimental results on a dataset consisting of staged bank robbery videos show that our algorithm provides very encouraging results when compared to human reviewers.