Extraction and Clustering of Motion Trajectories in Video
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Clustering Multidimensional Trajectories based on Shape and Velocity
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
A Coarse-to-Fine Strategy for Vehicle Motion Trajectory Clustering
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
On-line trajectory clustering for anomalous events detection
Pattern Recognition Letters
Compressing spatio-temporal trajectories
ISAAC'07 Proceedings of the 18th international conference on Algorithms and computation
Modification of the growing neural gas algorithm for cluster analysis
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Clustering of vehicle trajectories
IEEE Transactions on Intelligent Transportation Systems
Object Trajectory-Based Activity Classification and Recognition Using Hidden Markov Models
IEEE Transactions on Image Processing
Counting Pedestrians in Video Sequences Using Trajectory Clustering
IEEE Transactions on Circuits and Systems for Video Technology
A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance
IEEE Transactions on Circuits and Systems for Video Technology
Event Detection Using Trajectory Clustering and 4-D Histograms
IEEE Transactions on Circuits and Systems for Video Technology
A survey of video datasets for human action and activity recognition
Computer Vision and Image Understanding
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One of the more important issues in intelligent video surveillance systems is the ability to handle events from the motion of objects. Thus, the classification of the trajectory of an object of interest in a scene can give important information to higher levels of recognition. In this context, it is crucial to know what trajectories are commonly given in a model in order to detect suspect ones. This implies the study of a set of trajectories and grouping them into different categories. In this paper, we propose to adapt a bioinspired clustering algorithm, growing neural gas, that has been tested in other fields with high level of success due to its nice properties of being unnecessary to know a priori the number of clusters, robustness and that it can be adapted to different distributions. Due to the fact that human perception is based on atomic events, a segmentation of the trajectories is proposed. Finally, the obtained prototype sub-trajectories are grouped according to the sequence of the observed data to feed the model.