1. [Publications](/publications) 2. Gaussian Process Regression Flow for Analysis of Motion Trajectories # Gaussian Process Regression Flow for Analysis of Motion Trajectories ![](/sites/default/files/styles/wide/public/pubs/2011-11_Gaussian-Process-Regression/iccv_gprf.jpg?itok=-U1pAOxG) Recognition of motions and activities of objects in videos requires effective representations for analysis and matching of motion trajectories. In this paper, we introduce a new representation specifically aimed at matching motion trajectories. We model a trajectory as a continuous dense flow field from a sparse set of vector sequences using Gaussian Process Regression. Furthermore, we introduce a random sampling strategy for learning stable classes of motions from limited data. Our representation allows for incrementally predicting possible paths and detecting anomalous events from online trajectories. This representation also supports matching of complex motions with acceleration changes and pauses or stops within a trajectory. We use the proposed approach for classifying and predicting motion trajectories in traffic monitoring domains and test on several data sets. We show that our approach works well on various types of complete and incomplete trajectories from a variety of video data sets with different frame rates. ## Authors Kihwan Kim (Georgia Tech) Dongryeol Lee (Georgia Tech) Irfan Essa (Georgia Tech) ## Publication Date Tuesday, November 1, 2011 ## Published in [IEEE International Conference on Computer Vision (ICCV) 2011](http://www.iccv2011.org/) ## Research Area [Computer Vision](/research-area/computer-vision) ## External Links [Project page](http://www.cc.gatech.edu/cpl/projects/gprf/) ## Uploaded Files [gprf\_iccv2011.pdf](https://research.nvidia.com/sites/default/files/pubs/2011-11_Gaussian-Process-Regression/gprf_iccv2011.pdf "Open file in new window")9.35 MB ## Copyright This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to .