Detection and classification of motion boundaries
Eighteenth national conference on Artificial intelligence
Categorization and Learning of Pen Motion Using Hidden Markov Models
CRV '04 Proceedings of the 1st Canadian Conference on Computer and Robot Vision
Global distance-based segmentation of trajectories
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Interactive mining and semantic retrieval of videos
Proceedings of the 8th international workshop on Multimedia data mining: (associated with the ACM SIGKDD 2007)
Minimum description length approximation of digital curves
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
An algorithmic framework for segmenting trajectories based on spatio-temporal criteria
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Air traffic control: a local approach to the trajectory segmentation issue
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part III
An adaptive approach for online segmentation of multi-dimensional mobile data
MobiDE '12 Proceedings of the Eleventh ACM International Workshop on Data Engineering for Wireless and Mobile Access
Destination flow for crowd simulation
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Warped K-Means: An algorithm to cluster sequentially-distributed data
Information Sciences: an International Journal
Computer Vision and Image Understanding
Movement primitives as a robotic tool to interpret trajectories through learning-by-doing
International Journal of Automation and Computing
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We consider the segmentation of a trajectory into piece-wise polynomial parts, or possibly other forms. Segmentation is typically formulated as an optimization problem which trades off model fitting error versus the cost of introducing new segments. Heuristics such as split-and-merge are used to find the best segmentation. We show that for ordered data (eg., single curves or trajectories) the global optimum segmentation can be found by dynamic programming. The approach is easily extended to handle different segment types and top down information about segment boundaries, when available. We show segmentation resultsfor video sequences of a basketball undergoing gravitional and non-gravitaional motion.