A vision-based obstacle detection system for parking assistance

This paper proposes a monocular vision-based obstacle detection algorithm for parking assistance applications of advance safety vehicle by a rear camera. In order to efficiently detect various moving and stationary obstacles behind the vehicle, the feature of corner into the rear obstacles is firstly estimated by the Features from Accelerated Segment Test (FAST) corner detection methods. Then, the inverse perspective mapping (IPM) image can be used to determine whether every detected feature belongs to an obstacle candidate or to the ground. Based on these results, the segmentation and identification strategies are also proposed in order to determine the degree of collision risk and to filter out the non-hazardous candidates. Finally, the correct obstacle regions in IPM transformed image can be easily and quickly extracted. The system can provide a vision-based alert to the driver, helping to avoid collisions with obstacles behind the host vehicle. Through extensive experiments, we have shown that the rear obstacle detection system in typical urban situations can be used to efficiently extract obstacle regions markings. The proposed algorithm achieves high detecting rate and low computing power and is successfully implemented in ADI-BF561 600MHz dual core DSP.