The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Signal Processing for Computer Vision
Signal Processing for Computer Vision
Qualitative Spatiotemporal Analysis Using an Oriented Energy Representation
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
A Hierarchical Approach to Robust Background Subtraction using Color and Gradient Information
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Segmenting Foreground Objects from a Dynamic Textured Background via a Robust Kalman Filter
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A Texture-Based Method for Modeling the Background and Detecting Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
A System for Learning Statistical Motion Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modelling Crowd Scenes for Event Detection
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Behavior recognition via sparse spatio-temporal features
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Detecting Irregularities in Images and in Video
International Journal of Computer Vision
Spatio-temporal background models for outdoor surveillance
EURASIP Journal on Applied Signal Processing
Robust Real-Time Unusual Event Detection using Multiple Fixed-Location Monitors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scene modeling and change detection in dynamic scenes: A subspace approach
Computer Vision and Image Understanding
Detecting unusual activity in video
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Visual tracking using a pixelwise spatiotemporal oriented energy representation
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Long-Range spatio-temporal modeling of video with application to fire detection
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Spatiotemporal salience via centre-surround comparison of visual spacetime orientations
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Computer Vision and Image Understanding
Sparse representation for robust abnormality detection in crowded scenes
Pattern Recognition
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This paper proposes a novel approach to anomalous behaviour detection in video. The approach is comprised of three key components. First, distributions of spatiotemporal oriented energy are used to model behaviour. This representation can capture a wide range of naturally occurring visual spacetime patterns and has not previously been applied to anomaly detection. Second, a novel method is proposed for comparing an automatically acquired model of normal behaviour with new observations. The method accounts for situations when only a subset of the model is present in the new observation, as when multiple activities are acceptable in a region yet only one is likely to be encountered at any given instant. Third, event driven processing is employed to automatically mark portions of the video stream that are most likely to contain deviations from the expected and thereby focus computational efforts. The approach has been implemented with real-time performance. Quantitative and qualitative empirical evaluation on a challenging set of natural image videos demonstrates the approach's superior performance relative to various alternatives.