A 3-D Contour Segmentation Scheme Based on Curvature and Torsion
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Computer Graphics and Applications
Finding patterns in time series: a dynamic programming approach
Advances in knowledge discovery and data mining
Partial matching of planar polylines under similarity transformations
SODA '97 Proceedings of the eighth annual ACM-SIAM symposium on Discrete algorithms
Scaling up dynamic time warping for datamining applications
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Similarity Search for Multidimensional Data Sequences
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Rotation invariant distance measures for trajectories
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast gesture recognition based on a two-level representation
Pattern Recognition Letters
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
3D action recognition and long-term prediction of human motion
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
An efficient approach for human motion data mining based on curves matching
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part I
Decomposition and dictionary learning for 3D trajectories
Signal Processing
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Recent technological advances have made it possible to collect large amounts of 3D trajectory data. Such data play an essential role in numerous applications and are becoming increasingly important in mobile computing. One of the fundamental challenges in many of these application areas is the assessment of similarity between trajectories. As objects moving in a 3D space may often exhibit a similar motion pattern but may differ in location, orientation, and scale, the similarity assessment method employed must be invariant to these seven degrees of freedom. Previous work has addressed this problem primarily through local measures, such as curvature and torsion and has mostly concentrated on 2D trajectory data. This paper introduces a novel non iterative 3D trajectory matching framework that is translation, rotation, and scale invariant. We achieve this through the introduction of a pose normalization process that is based on physical principles, which incorporates both spatial and temporal aspects of trajectory data. We also introduce a new shape signature that utilizes the invariance that is achieved through pose normalization. The proposed scheme was tested both on simulated data and on real world data and has shown to offer improved robustness compared to local measures.