A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
VLSI Architecture for Motion Estimation using the Block-Matching Algorithm
EDTC '96 Proceedings of the 1996 European conference on Design and Test
A fast VLSI architecture for full-search variable block size motion estimation in MPEG-4 AVC/H.264
Proceedings of the 2005 Asia and South Pacific Design Automation Conference
In-vehicle vision processors for driver assistance systems
Proceedings of the 2008 Asia and South Pacific Design Automation Conference
Application development with the FlexWAFE real-time stream processing architecture for FPGAs
ACM Transactions on Embedded Computing Systems (TECS)
A Real-Time Low-Power Stereo Vision Engine Using Semi-Global Matching
ICVS '09 Proceedings of the 7th International Conference on Computer Vision Systems: Computer Vision Systems
Accurate hardware-based stereo vision
Computer Vision and Image Understanding
6D-vision: fusion of stereo and motion for robust environment perception
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
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Camera-based driver assistance systems have attracted the attention of all major automotive manufacturers in the past several years and are increasingly utilized to differentiate a vendor's vehicles from its competitors. The calculation of depth information and Motion Estimation can be considered as two fundamental image processing applications in these systems, which have already been evaluated in diverse research scenarios. However, in order to push these computation-intensive features towards series integration, future in-vehicle implementations must adhere to the automotive industry's strict power consumption and cost constraints. As an answer to this challenge, this paper presents a high-performance FPGA-based dense block matching solution, which enables the calculation of both object motion and the extraction of depth information on shared hardware resources. This novel single-design approach significantly reduces the amount of logic resources required, resulting in valuable cost and power savings. The acquired sensor information can be fusioned into 3D positions with an associated 3D motion vector, which enables a robust perception of the vehicle's environment. The modular implementation offers enhanced configuration features at design and execution time and achieves up to 418 GOPS at a moderate energy consumption of 10 Watts, providing a flexible solution for a future series integration.