Learning the distribution of object trajectories for event recognition
BMVC '95 Proceedings of the 6th British conference on Machine vision (Vol. 2)
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Event Detection and Analysis from Video Streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Event Detection and Summarization in Sports Video
CBAIVL '01 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'01)
Application of the Self-Organizing Map to Trajectory Classification
VS '00 Proceedings of the Third IEEE International Workshop on Visual Surveillance (VS'2000)
Event Detection by Eigenvector Decomposition Using Object and Frame Features
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 7 - Volume 07
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
Covariance Tracking using Model Update Based on Lie Algebra
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance
IEEE Transactions on Circuits and Systems for Video Technology
ViBe: A Universal Background Subtraction Algorithm for Video Sequences
IEEE Transactions on Image Processing
Multimedia analysis for ecological data
Proceedings of the 20th ACM international conference on Multimedia
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In this paper we propose a clustering-based approach for the analysis of fish trajectories in real-life unconstrained underwater videos, with the purpose of detecting behavioural events; in such a context, both video quality limitations and the motion properties of the targets make the trajectory analysis task for event detection extremely difficult. Our approach is based on the k-means clustering algorithm and allows to group similar trajectories together, thus providing a simple way to detect the most used paths and the most visited areas, and, by contrast, to identify trajectories which do not fall into any common clusters, therefore representing unusual behaviours. Our results show that the proposed approach is able to separate trajectory patterns and to identify those matching predefined behaviours or which are more likely to be associated to new/anomalous behaviours.