From image sequences towards conceptual descriptions
Image and Vision Computing
Learning flexible models from image sequences
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Visual surveillance in a dynamic and uncertain world
Artificial Intelligence - Special volume on computer vision
On the Use of Motion Concepts for Top-Down Control in Traffic Scenes
ECCV '90 Proceedings of the First European Conference on Computer Vision
Learning and Recognizing Human Dynamics in Video Sequences
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A state-based technique for the summarization and recognition of gesture
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Agent Orientated Annotation in Model Based Visual Surveillance
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Unsupervised scene analysis: a hidden Markov model approach
Computer Vision and Image Understanding
Visual inference of human emotion and behaviour
Proceedings of the 9th international conference on Multimodal interfaces
Incremental and adaptive abnormal behaviour detection
Computer Vision and Image Understanding
Automatic Learning of Conceptual Knowledge in Image Sequences for Human Behavior Interpretation
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Understanding dynamic scenes based on human sequence evaluation
Image and Vision Computing
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
Unsupervised scene analysis: A hidden Markov model approach
Computer Vision and Image Understanding
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We present a method for assessing the likelihood of a trajectory of an object through a scene consisting of a number of other objects. The closest points on the trajectory to the other objects are chosen as landmark points and at each landmark we calculate the probability of the interaction based on the speed and distance. Sequences of such probabilities are then sorted in increasing order. Finally a weighted sum of the first few elements in this weighted list is used to classify trajectories in a supervised learning framework.