Application of Lie Algebras to Visual Servoing
International Journal of Computer Vision - Special issue on image-based servoing
Recognition of Visual Activities and Interactions by Stochastic Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiobject Behavior Recognition by Event Driven Selective Attention Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing planned multiperson action
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence
Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence
Video-based event recognition: activity representation and probabilistic recognition methods
Computer Vision and Image Understanding - Special issue on event detection in video
Joint Recognition of Complex Events and Track Matching
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Using camera motion to identify types of American football plays
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Multiple agent event detection and representation in videos
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Beyond pixels: exploring new representations and applications for motion analysis
Beyond pixels: exploring new representations and applications for motion analysis
Robust Point Set Registration Using Gaussian Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-agent event recognition in structured scenarios
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Learning and recognizing complex multi-agent activities with applications to american football plays
WACV '12 Proceedings of the 2012 IEEE Workshop on the Applications of Computer Vision
Semantic analysis of soccer video using dynamic Bayesian network
IEEE Transactions on Multimedia
Similarity invariant classification of events by KL divergence minimization
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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We present a new method for multi-agent activity analysis and recognition that uses low level motion features and exploits the inherent structure and recurrence of motion present in multi-agent activity scenarios. Our representation is inspired by the need to circumvent the difficult problem of tracking in multi-agent scenarios and the observation that for many visual multi-agent recognition tasks, the spatiotemporal description of events irrespective of agent identity is sufficient for activity classification. We begin by learning generative models describing motion induced by individual actors or groups, which are considered to be agents. These models are Gaussian mixture distributions learned by linking clusters of optical flow to obtain contiguous regions of locally coherent motion. These possibly overlapping regions or segments, known as motion patterns are then used to analyze a scene by estimating their spatial and temporal relationships. The geometric transformations between two patterns are obtained by iteratively warping one pattern onto another, whereas the temporal relationships are obtained from their relative times of occurrence within videos. These motion segments and their spatio-temporal relationships are represented as a graph, where the nodes are the statistical distributions, and the edges have geometric transformations between motion patterns transformed to Lie space, as their attributes. Two activity instances are then compared by estimating the cost of attributed inexact graph matching. We demonstrate the application of our framework in the analysis of American football plays, a typical multi-agent activity. The performance analysis of our method shows that it is feasible and easily generalizable.