Object Recognition and Pose Estimation on Embedded Hardware : SURF-Based System Designs Accelerated by FPGA Logic

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 fe...

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Bibliographic Details
Published in:International journal of reconfigurable computing 2012-01, Vol.2012 (2012), p.1-16
Main Authors: Schaeferling, Michael, Hornung, Ulrich, Kiefer, Gundolf
Format: Article
Language:English
Subjects:
Algorithms
Augmented reality
Cameras
Computation
Field programmable gate arrays
Hardware
Object recognition
Order picking
Platforms
Software
Studies
Systems design
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Summary: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 medium-size 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.
ISSN:1687-7195
1687-7209
DOI:10.1155/2012/368351