Optical flow object detection, motion estimation, and tracking on moving vehicles using wavelet decompositions

Abstract

Optical flow-based tracking methods offer the promise of precise, accurate, and reliable analysis of motion, but they suffer from several challenges such as elimination of background movement, estimation of flow velocity, and optimal feature selection. Wavelet approximations can offer similar benefits and retain spatial information at coarser scales, while optical flow estimation increases with the reduction of finer details of moving objects. Optical flow methods often suffer from significant computational overload. In this study, we have investigated the necessary processing steps to increase detection and estimation accuracy, while effectively reducing computation time through the reduction of the image frame size. We have implemented an object tracking algorithm using the optical flow calculated from a phase change between representative coarse wavelet coefficients in subsequent image frames. We have also compared phasebased optical flow with two versions of intensity-based optical flow to determine which method produces superior results under specific operational conditions. The investigation demonstrates the feasibility of using phase-based optical flow with wavelet approximations for object detection and tracking of low resolution aerial vehicles. We also demonstrate that this method can work in tandem with feature-based tracking methods to increase tracking accuracy.