Hardware/software architecture of an algorithm for vision-based real-time vehicle detection in dark environments

Hardware/software partitioning of algorithms is gaining more and more importance in order to benefit from the advantages of both worlds. Pure software implementations are easy to change but the processing time is rather high. By contrast pure hardware implementations usually result in faster processing due to inherent parallelism but they do not offer the necessary flexibility for quick changes and adaptions. In this paper the hardware/software co-design of a self-developed algorithm to detect cars by their taillights as well as its implementation on an embedded system (FPGA) is presented. Instead of utilizing expensive sensors such as RADAR which also can be used to detect obstacles in dark environments, the detection method presented here is based solely on grayscale images taken by a low-cost on-board camera which was mounted on a moving vehicle. Only computationally intense parts - namely pixel or sliding window operations - are implemented in hardware to achieve the necessary real-time requirements. The remainder of the algorithm - the so called higher level application code - is running on standard embedded CPU cores. With this architecture it is possible to process the incoming video-stream (25 frames/s) and detect cars in real-time on an embedded system.