Detecting Regions of Interest in Dynamic Scenes with Camera Motions
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 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 predictions than
previously proposed RBF-based approaches.