Hierarchical locally adaptive multigrid motion estimation for surveillance applications

In this communication we address the problem of detection and tracking of moving objects for surveillance or occupant detection systems. The primary goal in this framework is the motion estimation of the extracted foreground. To overcome the drawbacks characteristic of classical block matching techniques, this contribution presents a new feature based hierarchical locally adaptive multigrid (HLAM) block matching motion estimation technique based on a foreground detection procedure using an adaptive recursive temporal lowpass filter. It leads to a robust and precise motion field estimation, close to the true motion in the scene. The simulation results highlight the superior performance of the proposed method. It yields better performance than the classical exhaustive search (ES) and the modified three-step search (MTSS) technique in terms of the peak signal-to-noise ratio (PSNR).