Detecting Regions of Interest in Dynamic Scenes with Camera Motions

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.


Kihwan Kim (NVIDIA)
Dongryeol Lee (Georgia Tech)
Irfan Essa (Georgia Tech)

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