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Paper in IEEE CVPR 2012: “Detecting Regions of Interest in Dynamic Scenes with Camera Motions”

Detecting Regions of Interest in Dynamic Scenes with Camera Motions

  • K. Kim, D. Lee, and I. Essa (2012), “Detecting Regions of Interest in Dynamic Scenes with Camera Motions,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012. [PDF] [WEBSITE] [VIDEO] [DOI] [BLOG] [BIBTEX]
    @inproceedings{2012-Kim-DRIDSWCM,
      Author = {Kihwan Kim and Dongreyol Lee and Irfan Essa},
      Blog = {http://prof.irfanessa.com/2012/04/09/paper-cvpr2012/},
      Booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      Date-Added = {2012-04-09 22:37:06 +0000},
      Date-Modified = {2013-10-22 18:53:11 +0000},
      Doi = {10.1109/CVPR.2012.6247809},
      Pdf = {http://www.cc.gatech.edu/~irfan/p/2012-Kim-DRIDSWCM.pdf},
      Publisher = {IEEE Computer Society},
      Title = {Detecting Regions of Interest in Dynamic Scenes with Camera Motions},
      Url = {http://www.cc.gatech.edu/cpl/projects/roi/},
      Video = {http://www.youtube.com/watch?v=19BMwDMCSp8},
      Year = {2012},
      Bdsk-Url-1 = {http://www.cc.gatech.edu/cpl/projects/roi/},
      Bdsk-Url-2 = {http://dx.doi.org/10.1109/CVPR.2012.6247809}}

Abstract

We present a method to detect the regions of interests in moving camera views of dynamic scenes with multiple mov- ing objects. We start by extracting a global motion tendency that reflects the scene context by tracking movements of objects in the scene. We then use Gaussian process regression to represent the extracted motion tendency as a stochastic vector field. The generated stochastic field is robust to noise and can handle a video from an uncalibrated moving camera. We use the stochastic field for predicting important future regions of interest as the scene evolves dynamically.

We evaluate our approach on a variety of videos of team sports and compare the detected regions of interest to the camera motion generated by actual camera operators. Our experimental results demonstrate that our approach is computationally efficient, and provides better prediction than those of previously proposed RBF-based approaches.

Presented at: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2012, Providence, RI, June 16-21, 2012

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