FPGA-GPU architecture for kernel SVM pedestrian detection

We present a real-time multi-sensor architecture for video-based pedestrian detection used within a road side unit for intersection assistance. The entire system is implemented on available PC hardware, combining a frame grabber board with embedded FPGA and a graphics card into a powerful processing network. Giving classification performance top priority, we use HOG descriptors with a Gaussian kernel support vector machine. In order to achieve real-time performance, we propose a hardware architecture that incorporates FPGA-based feature extraction and GPU-based classification. The FPGA-GPU pipeline is managed by a multi-core CPU that further performs sensor data fusion. Evaluation on the INRIA benchmark database and an experimental study on a real-world intersection using multi-spectral hypothesis generation confirm state-of-the-art classification and real-time performance.