A general method for human activity recognition in video
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Spectral clustering with eigenvector selection
Pattern Recognition
Video event segmentation and visualisation in non-linear subspace
Pattern Recognition Letters
A framework for visualization and exploration of events
Information Visualization
Feature fusion for basic behavior unit segmentation from video sequences
Robotics and Autonomous Systems
Joint trajectory tracking and recognition based on bi-directional nonlinear learning
Image and Vision Computing
Exploiting temporal statistics for events analysis and understanding
Image and Vision Computing
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Towards Generic Detection of Unusual Events in Video Surveillance
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Bayesian Bio-inspired Model for Learning Interactive Trajectories
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Learning People Trajectories Using Semi-directional Statistics
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Finding long and similar parts of trajectories
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
A dynamic hierarchical clustering method for trajectory-based unusual video event detection
IEEE Transactions on Image Processing
Data Driven Evaluation of Crowds
MIG '09 Proceedings of the 2nd International Workshop on Motion in Games
Trajectory analysis in natural images using mixtures of vector fields
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Detection of user-defined, semantically high-level, composite events, and retrieval of event queries
Multimedia Tools and Applications
Vs-star: A visual interpretation system for visual surveillance
Pattern Recognition Letters
Cooperative object tracking and composite event detection with wireless embedded smart cameras
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
Anomalous video event detection using spatiotemporal context
Computer Vision and Image Understanding
Event detection based on a pedestrian interaction graph using hidden Markov models
PIA'11 Proceedings of the 2011 ISPRS conference on Photogrammetric image analysis
Pattern discovery for video surveillance
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
A two-stage genetic algorithm for automatic clustering
Neurocomputing
Activity recognition via classification constrained diffusion maps
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
3D scene modeling for activity detection
ER'05 Proceedings of the 24th international conference on Perspectives in Conceptual Modeling
Event detection in underwater domain by exploiting fish trajectory clustering
Proceedings of the 1st ACM international workshop on Multimedia analysis for ecological data
On the use of a minimal path approach for target trajectory analysis
Pattern Recognition
A rule-based event detection system for real-life underwater domain
Machine Vision and Applications
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We develop an event detection framework that has two significant advantages over past work. First, we introduce an extended set of time-wise and object-wise statistical features including not only the trajectory coordinates but also the histograms and HMM based representations of object's speed, orientation, location, size, and aspect ratio. These features enable detection of events that cannot be detected with the existing trajectory features reported so far. Second, we introduce a spectral clustering algorithm that can automatically estimate the optimal number of clusters. First, we construct feature-wise affinity matrices from the pair-wise similarity scores of objects using the extended set of features. To determine the usual events, we apply eigen-vector decomposition and obtain object clusters. We show that the number of eigenvectors used in the decomposition is proportional to the optimal number of clusters. Unlike the conventional approaches that try to fit predefined models to events, we analyze the conformity of objects using affinity matrices to find the unusual events. We improve the feature selection process by incorporating feature variances. We prove that the clustering stage is not adversely affected by high dimensionality of data space. Our simulations with synthetic and real data reveal that the proposed detection methods accurately detect usual and unusual events.