Detecting Regions of Interest in Dynamic Scenes with Camera Motions

 

To appear at IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2012 Providence, Rhode Island

Authors

Kihwan Kim

NVIDIA Research

Dongreyol Lee

Georgia Institute of Technology

Irfan Essa

Georgia Institute of Technology

Abstract

We present a method to detect the regions of interests in moving camera views of dynamic scenes with multiple moving 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 noiseand 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 predictions than previously proposed RBF-based approaches.

Paper

Citation
Kihwan Kim, Dongryeol Lee, Irfan Essa (2012), "Detecting Regions of Interest in Dynamic Scenes with Camera Motions", To Appear in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, IEEE Computer Society, June 2012.
@inproceedings{Kim:2012:GP-ROI,
       Author = {Kihwan Kim and Dongryeol Lee and Irfan Essa},
       Booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
       Month = {June},
       Organization = {IEEE Computer Society},
       Title = {Detecting Regions of Interest in Dynamic Scenes with Camera Motions},
       Year = {2012},
}
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