Toward Real-time Vehicle Detection Using Stereo Vision and an Evolutionary Algorithm

A new approach for vehicle detection and distance estimation based on stereo vision and evolutionary algorithm (SEA) is described in this paper. First, we reuse our recent work on FPGA implementation of census-based correlations for stereo matching. Next, the SEA uses the gray scale left image and disparity information obtained from the FPGA system to detect the preceding vehicle and estimate its distance. This paper introduces an effective fitness function that allows our proposed method to have an improved performance and higher accuracy when compared with the existing evolutionary algorithm (EA) based methods. A new crossover type, tournament crossover, is introduced to reduce the convergence time of our proposed. This paper also introduces a new approach for estimating the fitness function parameters. This estimation differs from the traditional EA because these parameters were generally created via experiments. Moreover, the processing time and accuracy of SEA can be improved by converting the global search to the local search with V disparity map. The robust experiments have proved that SEA successfully detects vehicles in front and sustains noise from different objects appearing along the road. The detection range is 10m-140m, the detection rate is 95 % and the average processing-time is approximately 31 ms/frame on CPU. These results prove that SEA is suitable for a real-time system.