Time-Delayed Correlation Analysis for Multi-Camera Activity Understanding
International Journal of Computer Vision
A hybrid moving object detection method for aerial images
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Abnormality detection using low-level co-occurring events
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International Journal of Computer Vision
Deriving implicit indoor scene structure with path analysis
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness
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International Journal of Computer Vision
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IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
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Proceedings of the 27th Conference on Image and Vision Computing New Zealand
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M4L: Maximum margin Multi-instance Multi-cluster Learning for scene modeling
Pattern Recognition
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Proceedings of the second ACM SIGCOMM workshop on Mobile cloud computing
Summarizing high-level scene behavior
Machine Vision and Applications
International Journal of Computer Vision
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We propose a novel method to model and learn the scene activity, observed by a static camera. The proposed model is very general and can be applied for solution of a variety of problems. The motion patterns of objects in the scene are modeled in the form of a multivariate nonparametric probability density function of spatiotemporal variables (object locations and transition times between them). Kernel Density Estimation is used to learn this model in a completely unsupervised fashion. Learning is accomplished by observing the trajectories of objects by a static camera over extended periods of time. It encodes the probabilistic nature of the behavior of moving objects in the scene and is useful for activity analysis applications, such as persistent tracking and anomalous motion detection. In addition, the model also captures salient scene features, such as the areas of occlusion and most likely paths. Once the model is learned, we use a unified Markov Chain Monte Carlo (MCMC)-based framework for generating the most likely paths in the scene, improving foreground detection, persistent labeling of objects during tracking, and deciding whether a given trajectory represents an anomaly to the observed motion patterns. Experiments with real-world videos are reported which validate the proposed approach.