Algorithm and architecture design of a knowledge-based vehicle tracking for intelligent cruise control
The paper exploits a vision-based intelligent vehicle cruise control system from the application level to the architecture level. Firstly, design considerations of the system are addressed in both computing power and accuracy aspects. Secondly, we present an efficient knowledge-based front-vehicle tracking algorithm. The algorithm yields below 5% error rate that outperforms the state-of-the-arts. Thirdly, a run-length-based algorithm optimization flow is introduced. Finally, specific hardware architecture is developed. It achieves 1280×960/80FPS and 4096×2160/10FPS requirements for multi-vehicle tracking tasks.