Gaussian Process Regression Flow

  for Analysis of Motion Trajectories


To appear at IEEE International Conference on Computer Vision (ICCV) 2011 Barcelona, Spain


Kihwan Kim

Georgia Institute of Technology

Dongreyol Lee

Georgia Institute of Technology

Irfan Essa

Georgia Institute of Technology


AnalysisRecognition 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



       Author = {Kihwan Kim and Dongryeol Lee and Irfan Essa},
       Booktitle = {Proceedings of IEEE International Conference on Computer Vision (ICCV)},
       Month = {November},
       Organization = {IEEE Computer Society},
       Title = {Gaussian Process Regression Flow for Analysis of Motion Trajectories},
       Year = {2011},


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This research is supported by:
  • Kitware and DARPA
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  • TBA

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