Currently, the automotive industry is actively seeking generic obstacle sensors based on monocular vision that are able to run on low frequency central processing units (CPUs). The authors have tackled the challenge of designing a vision-based obstacle detection system using a common in-vehicle micro controller: an 80 MHz 32 bits RISC CPU. This system uses a single wide angle rear camera commercially available. To insure real-time obstacle detection, we proposed a novel fast feature-point extractor applied along a 1D signal. The authors named it transition. It is 100 times faster than Harris and relevant on man-made objects. The authors present feature extraction results and demonstrate that it can be used to detect various types of obstacles with an 80MHz CPU.