Fast communication: Dominant sets clustering for image retrieval
Signal Processing
Segment Model Based Vehicle Motion Analysis
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Hierarchical Matching of 3D Pedestrian Trajectories for Surveillance Applications
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Syntactic matching of trajectories for ambient intelligence applications
IEEE Transactions on Multimedia
Environmental Modelling & Software
Adaptive human motion analysis and prediction
Pattern Recognition
Clustering of trajectories in video surveillance using growing neural gas
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation - Volume Part I
Trajectory Analysis and Semantic Region Modeling Using Nonparametric Hierarchical Bayesian Models
International Journal of Computer Vision
A novel trajectory clustering approach for motion segmentation
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Annotated free-hand sketches for video retrieval using object semantics and motion
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
Similarity in (spatial, temporal and) spatio-temporal datasets
Proceedings of the 15th International Conference on Extending Database Technology
Learning common behaviors from large sets of unlabeled temporal series
Image and Vision Computing
On the use of a minimal path approach for target trajectory analysis
Pattern Recognition
Learning to rank biological motion trajectories
Image and Vision Computing
TODMIS: mining communities from trajectories
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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High-level semantic understanding of vehicle motion behaviors is often based on vehicle motion trajectory clustering. In this paper, we propose an effective trajectory clustering framework in which a coarse-to-fine strategy is taken. Our framework consists of four stages: trajectory smoothing, feature extraction, trajectory coarse clustering and trajectory fine clustering. Wavelet decomposition is imposed on raw trajectories to reduce noise in the trajectory smoothing stage. Besides the commonly used positional feature, a novel feature called trajectory directional histogram is proposed to describe the statistic directional distribution of a trajectory in the feature extraction stage. Both coarse clustering and fine clustering are based on a novel graphtheoretic clustering algorithm called dominant-set clustering, but they deal with different trajectory features. Experiments in our pre-labeled trajectory database demonstrate that the proposed trajectory clustering framework possesses a very high accuracy.