Accelerating Histograms of Oriented Gradients descriptor extraction for pedestrian recognition

Pedestrian recognition is an emerging visual computing application for embedded systems. In one usage model, a vehicle mounted camera acquires image from road and a pedestrian recognition system automatically recognizes and alarms information on the road preventing traffic accidents. Achieving this in software on embedded systems requires significant compute processing for object recognition. In this paper, we identify the hotspot function of the workload on an embedded system that motivates acceleration and present the detailed design of a hardware accelerator for Histograms of Oriented Gradients descriptor extraction. We also quantify the performance and area efficiency of the hardware accelerator. Our analysis shows that hardware acceleration has the potential to improve the hotspot function. As a result, user response time can be reduced significantly.