Gaussian Process Regression Flow

  for Analysis of Motion Trajectories

 

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

Authors

Kihwan Kim

Georgia Institute of Technology

Dongreyol Lee

Georgia Institute of Technology

Irfan Essa

Georgia Institute of Technology

Abstract

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

Paper

Poster

Citation
@inproceedings{Kim:2011:Gaussian-Process,
       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},
}
Video

 

Slides
Slide will be uploaded soon Download slides
Demo / Source / Dataset
TBA
Funding
This research is supported by:
  • Kitware and DARPA
Media Coverage / Links to similar projects
  • TBA
Copyright

The documents contained in these directories are included by the contributing authors as a means to ensure timely dissemination of scholarly and technical work on a non-commercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without explicit permission of the copyright holder.

 

View My Stats