Learning the distribution of object trajectories for event recognition
BMVC '95 Proceedings of the 6th British conference on Machine vision (Vol. 2)
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Multi Feature Path Modeling for Video Surveillance
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Symbolic representation and retrieval of moving object trajectories
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
3D trajectory matching by pose normalization
Proceedings of the 13th annual ACM international workshop on Geographic information systems
A Coarse-to-Fine Strategy for Vehicle Motion Trajectory Clustering
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
A Dynamic Programming Technique for Classifying Trajectories
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
3D action recognition and long-term prediction of human motion
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Semantic-Based Surveillance Video Retrieval
IEEE Transactions on Image Processing
| Hi-index | 0.00 |
In this paper we propose a string-based approach to effectively represent trajectories in the 3D space. The strategy is coupled with a syntactical matching algorithm that allows evaluating the similarity of the retrieved data with pre-stored templates. The symbolic representation of the trajectory, is the core of the proposed system, which helps discriminating among different tracks using a modified version of the edit-distance. The hierarchical application of the algorithm on the spatial and temporal components helps detecting anomalous trajectories, and has proven to be robust in automatically learning new instances or classes of paths. We present the results achieved by performing a number of tests in an indoor lab used as a testbed for assisted living applications. The algorithm can discriminate among different classes of trajectories and can recognize actions and detect anomalies within the same class.