GPU Accelerated ACF Detector
Wiebe Van Ranst, Floris De Smedt, Toon Goedemé · 2018
The field of pedestrian detection has come a long way in recent decades. In terms of accuracy, the current state-of-the-art is hands down reached by Deep Learning methods. However in terms of running speed this is not always the case, traditional methods are often still faster than their Deep Learning counterparts. This is especially true on embedded hardware, embedded platforms are often used in applications that require realtime performance while at same the time having to make do with a limited amount of resources. In this paper we present a GPU implementation of the ACF pedestrian detector and compare it to current Deep Learning approaches (YOLO) on both a desktop GPU as well as the Jetson TX2 embedded GPU platform.