Using synthetic basis feature descriptor for motion estimation

Abstract

Development of advanced driver assistance systems has become an important focus for automotive industry in recent years. Within this field, many computer vision–related functions require motion estimation. This article discusses the implementation of a newly developed SYnthetic BAsis (SYBA) feature descriptor for matching feature points to generate a sparse motion field for analysis. Two motion estimation examples using this sparse motion field are presented. One uses motion classification for monitoring vehicle motion to detect abrupt movement and to provide a rough estimate of the depth of the scene in front of the vehicle. The other one detects moving objects for vehicle surrounding monitoring to detect vehicles with movements that could potentially cause collisions. This algorithm detects vehicles that are speeding up from behind, slowing down in the front, changing lane, or passing. Four videos are used to evaluate these algorithms. Experimental results verify SYnthetic BAsis’ performance and the feasibility of using the resulting sparse motion field in embedded vision sensors for motion-based driver assistance systems.