Guanglin Ma

A real-time rear view camera based obstacle detection

This paper presents a robust real-time rear-view camera based object detection algorithm for backup aid and parking assist applications. The system is capable of handling the challenges of stationary as well as moving objects in rear view of the host vehicle, utilizing a single car-mounted rear-view fish-eye camera. A motion-based and edge-based object detection algorithm was developed in order to detect near distance objects and distant objects respectively. Furthermore, a free-space detection algorithm was developed which is used to detect areas where no objects appeared in front of the vehicle path. A tracker-based fusion algorithm was used to track and fuse detection results of individual detection algorithms. Experiments have been carried out by applying the proposed algorithm on prerecorded sequences as well as within a test vehicle and thus in a closed loop environment. The experimental results indicate promising detection performance.

A REAL-TIME DETECTION ALGORITHM FOR VISION-BASED PEDESTRIAN PROTECTION

This paper discusses the robust, real-time detection of stationary and moving pedestrians utilizing a single car-mounted monochrome camera. First, the system detects potential pedestrians above the ground plane by combining conventional Inverse Perspective Mapping (IPM)-based obstacle detection with the vertical 1D profile evaluation of the IPM detection result. Usage of the vertical profile increases the robustness of detection in low-contrast images as well as the detection of distant pedestrians significantly. A fast digital image stabilization algorithm is used to compensate for erroneous detections whenever the flat ground plane assumption is an inaccurate model of the road surface. Finally, a low-level pedestrian-oriented segmentation and fast symmetry search on the leg region of pedestrians is also presented. A novel approach termed Pedestrian Detection Strip (PDS) is used to improve the calculation time by a factor of six compared to conventional approaches.