Multi-Resolution Estimation of Optical Flow on Vehicle Tracking under Unpredictable Environments
Viet Dung Do, Dong-Min Woo · Advanced science and technology letters · 2016
This paper proposes a vehicle tracking system that can effectively solve a multi-resolution problem under unpredictable environments. Our system uses Scale-Invariant Feature Transform (SIFT) algorithm as the feature detector. The pyramidal Lucas Kanade optical flow algorithm using our feature tracking system is then implemented. The information after the estimation of optical flow can be used in the later tracking and detecting problems. For the purpose of evaluation, we choose the Autonomous Agents for On-Scene Networked Incident Management (ATON) project's highway video files which include moving shadows. Experimental results show good performance of our multi-resolution optical flow system. It is confirmed that the computed optical flow has very small errors in minimum eigenvalues of optical flow equations.