Determination of optical flow and its discontinuities using non-linear diffusion
ECCV '94 Proceedings of the third European conference on Computer Vision (Vol. II)
Learning the distribution of object trajectories for event recognition
BMVC '95 Proceedings of the 6th British conference on Machine vision (Vol. 2)
Learning variable-length Markov models of behavior
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
The Acquisition and Use of Interaction Behavior Models
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Improved Adaptive Gaussian Mixture Model for Background Subtraction
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Language Label Learning for Visual Concepts Discovered from Video Sequences
Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint
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Experiments in infant category formation indicate a strong role for temporal continuity and change in perceptual categorization. Computational approaches to model discovery in vision have traditionally focused on static images, with appearance features such as shape playing an important role. In this work, we consider integrating agent behaviors with shape for the purpose of agent discovery. Improved algorithms for video segmentation and tracking under occlusion enable us to construct models that characterize agents in terms of motion and interaction with other objects. We present a preliminary approach for discovering agents based on a combination of appearance and motion histories. Using uncalibrated camera images, we characterize objects discovered in the scene by their shape and motion attributes, and cluster these using agglomerative hierarchical clustering. Even with very simple feature sets, initial results suggest that the approach forms reasonable clusters for diverse categories such as people, and for very distinct clusters (animals), and performs above average on other classes.