Efficient and high-performance pedestrian detection implementation for intelligent vehicles

Implementing pedestrian detection real-time embedded systems remains a major challenge. Detecting pedestrians in an advanced driver assistance system requires a lot of time and resources. The method based on Multi-scale Covariance (MSRCD) descriptor and Support Vector Machine (SVM) is one of the most effective approaches to perform person detection systems. However, such implementation is difficult to be executed in real-time on embedded systems. This paper presents three improvements to adapt the solution based on the MSRCD descriptor and SVM classifier for embedded pedestrian detection. First, a new features combination capable to provide the most accurate description at minimum processing time for MSRCD is proposed. Second, to speed up the SVM classification a new approach that associates SVM with mean technic and Euclidian distance is proposed. Third, parallel implementation is exploited to accelerate processing time on multi-core architectures. The software implementation is performed using the INRIA data set. 18.94% processing time speed-up, 48.21% less memory usage and 2.38% improved detection accuracy are achieved using the proposed descriptor. 58.22% processing time speed-up is obtained for the proposed classifier while keeping the same testing accuracy. The parallel implementation is performed using zynq platform based on ARM Cortex-A9. The obtained results confirmed the effectiveness of the parallelization to accelerate the computing time about 3 times the original sequential processing