FPGA-Based Real-Time Pedestrian Detection on High-Resolution Images

This paper focuses on real-time pedestrian detection on Field Programmable Gate Arrays (FPGAs) using the Histograms of Oriented Gradients (HOG) descriptor in combination with a Support Vector Machine (SVM) for classification as a basic method. We propose to process image data at twice the pixel frequency and to normalize blocks with the L1-Sqrt-norm resulting in an efficient resource utilization. This implementation allows for parallel computation of different scales. Combined with a time-multiplex approach we increase multiscale capabilities beyond resource limitations. We are able to process 64 high resolution images (1920 × 1080 pixels) per second at 18 scales with a latency of less than 150 u s. 1.79 million HOG descriptors and their SVM classifications can be calculated per second and per scale, which outperforms current FPGA implementations by a factor of 4.