Efficient real-time local optical flow estimation by means of integral projections

Abstract

In this paper we present an approach for the efficient computation of optical flow fields in real-time and provide implementation details. Proposing a modification of the popular Lucas-Kanade energy functional based on integral projections allows us to speed up the method notably. We show the potential of this method which can compute dense flow fields of 640×480 pixels at a speed of 4 fps in a GPU implementation based on the OpenCL framework. Working on sparse optical flow fields of up to 17,000 points, we reach execution times of 70 fps. Optical flow methods are used in many different areas, the proposed method speeds up current surveillance algorithms used for scene description and crowd analysis or Augmented Reality and robot navigation applications.