Moving objects detection using classifying object proposals for driver assistance system
Kunyao Chen, Subarna Tripathi, Youngbae Hwang, Truong Q. Nguyen · 2016
We present a new framework for driver assistance system, detecting moving objects in the street scene. Our algorithm supports a wide range of objects including vehicles, cyclists, pedestrian etc. Based on candidate bounding boxes detected by object proposals, our classifier only responds to the objects truly moving, which is more practical for real applications. Using unified features of color, structure and motion information, our system runs in real time with 66% detection rate in CamVid dataset. In addition, our method can be implemented efficiently with pipelined function blocks.