Hardware implementation of real-time pedestrian detection system

Histogram of oriented gradients (HOG) descriptor and support vector machine (SVM) classifier have been considered as a very efficient and robust tools used in the development of pedestrian detection systems. However, the enormous calculations make it difficult to meet real-time requirement specifically for advanced driver assistance system applications. The latter requires higher frame rate and resolution. In the present work, efficient pipelined architecture founded on cell-based scanning and simultaneous linear SVM classification is highlighted. The proposed real-time pedestrian detection architecture performs without external memories, and only 3 x 3 pixels are processed using two line buffers. Indeed, intermediate results will contribute directly to build the cell histograms. Consequently, it allows to reduce the required memory and to speed up the HOG feature extraction procedure. The implementation results on a heterogeneous platform show that the proposed architecture achieves an efficient pedestrian detection for full-HD video recordings (1080 x 1920 pixels) at 60 fps with reduced hardware resource consumption. Using the same hardware resources at 444 MHz, the proposed system turns out to handle a HD video at 180 fps.