State-of-the-art object recognition and pose estimation systems often utilize point feature algorithms, which in turn usually require the computing power of conventional PC hardware. In this paper, we describe two embedded systems for object detection and pose estimation using sophisticated point features. The feature detection step of the "Speeded-up Robust Features (SURF)" algorithm is accelerated by a special IP core. The first system performs object detection and is completely implemented in a single mediumsize Virtex-5 FPGA. The second system is an augmented reality platform, which consists of an ARM-based microcontroller and intelligent FPGA-based cameras which support the main system.
A SURF-based object recognition and pose estimation embedded system was introduced in REF .
Object recognition and pose estimation on embedded hardware: SURF-based system designs accelerated by FPGA logic
Int. J. Reconfigurable Comput.
Int. J. Reconfigurable Comput.
Computer Science