research-article

Hardware/software architecture of an algorithm for vision-based real-time vehicle detection in dark environments

Authors:

Nicolas Alt,

Christopher Claus,

Walter StecheleAuthors Info & Claims

DATE '08: Proceedings of the conference on Design, automation and test in Europe

Pages 176 - 181

https://doi.org/10.1145/1403375.1403419

Published: 10 March 2008 Publication History

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Abstract

Hardware/software partitioning of algorithms is gaining more and more importance in order to benefit from the advantages of both worlds. Pure software implementations are easy to change but the processing time is rather high. By contrast pure hardware implementations usually result in faster processing due to inherent parallelism but they do not offer the necessary flexibility for quick changes and adaptions. In this paper the hardware/software co-design of a self-developed algorithm to detect cars by their taillights as well as its implementation on an embedded system (FPGA) is presented. Instead of utilizing expensive sensors such as RADAR which also can be used to detect obstacles in dark environments, the detection method presented here is based solely on grayscale images taken by a low-cost on-board camera which was mounted on a moving vehicle. Only computationally intense parts - namely pixel or sliding window operations - are implemented in hardware to achieve the necessary real-time requirements. The remainder of the algorithm - the so called higher level application code - is running on standard embedded CPU cores. With this architecture it is possible to process the incoming video-stream (25 frames/s) and detect cars in real-time on an embedded system.

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Liu YLian ZDing JGuo T(2019)Multiple Objects Tracking Based Vehicle Speed Analysis with Gaussian Filter from Drone VideoIntelligence Science and Big Data Engineering. Visual Data Engineering10.1007/978-3-030-36189-1_30(362-373)Online publication date: 29-Nov-2019
https://doi.org/10.1007/978-3-030-36189-1_30
Chen ABiglari-Abhari MWang KBouzerdoum ATivive F(2018)Convolutional neural network acceleration with hardware/software co-designApplied Intelligence10.1007/s10489-017-1007-z48:5(1288-1301)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1007/s10489-017-1007-z
Shreejith SAnshuman BFahmy SFanucci LTeich J(2016)Accelerated artificial neural networks on FPGA for fault detection in automotive systemsProceedings of the 2016 Conference on Design, Automation & Test in Europe10.5555/2971808.2971817(37-42)Online publication date: 14-Mar-2016
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Hardware/software architecture of an algorithm for vision-based real-time vehicle detection in dark environments

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Information

Published In

DATE '08: Proceedings of the conference on Design, automation and test in Europe

March 2008

1575 pages

ISBN:9783981080131

DOI:10.1145/1403375

General Chair:
Donatella Sciuto
Politecnico di Milano, Italy

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 March 2008

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DATE '08

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EDAA
SIGDA
The Russian Academy of Sciences

DATE '08: Design, Automation and Test in Europe

March 10 - 14, 2008

Munich, Germany

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Overall Acceptance Rate 518 of 1,794 submissions, 29%

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Citations

Cited By

View all

Liu YLian ZDing JGuo T(2019)Multiple Objects Tracking Based Vehicle Speed Analysis with Gaussian Filter from Drone VideoIntelligence Science and Big Data Engineering. Visual Data Engineering10.1007/978-3-030-36189-1_30(362-373)Online publication date: 29-Nov-2019
https://doi.org/10.1007/978-3-030-36189-1_30
Chen ABiglari-Abhari MWang KBouzerdoum ATivive F(2018)Convolutional neural network acceleration with hardware/software co-designApplied Intelligence10.1007/s10489-017-1007-z48:5(1288-1301)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1007/s10489-017-1007-z
Shreejith SAnshuman BFahmy SFanucci LTeich J(2016)Accelerated artificial neural networks on FPGA for fault detection in automotive systemsProceedings of the 2016 Conference on Design, Automation & Test in Europe10.5555/2971808.2971817(37-42)Online publication date: 14-Mar-2016
https://dl.acm.org/doi/10.5555/2971808.2971817
Chien CHang HTseng DChen Y(2016)An image based overexposed taillight detection method for frontal vehicle detection in night vision2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)10.1109/APSIPA.2016.7820881(1-9)Online publication date: Dec-2016
https://doi.org/10.1109/APSIPA.2016.7820881
Parate SV SSwarup S(2014)Night time rear end collision avoidance system using SMPTE-C standard and VWVF2014 IEEE International Conference on Vehicular Electronics and Safety10.1109/ICVES.2014.7063717(17-21)Online publication date: Dec-2014
https://doi.org/10.1109/ICVES.2014.7063717
Shreejith SFahmy S(2014)Zero latency encryption with FPGAs for secure time-triggered automotive networks2014 International Conference on Field-Programmable Technology (FPT)10.1109/FPT.2014.7082788(256-259)Online publication date: Dec-2014
https://doi.org/10.1109/FPT.2014.7082788
Wang JSun XGuo J(2013)A Region Tracking-Based Vehicle Detection Algorithm in Nighttime Traffic ScenesSensors10.3390/s13121647413:12(16474-16493)Online publication date: 2-Dec-2013
https://doi.org/10.3390/s131216474
Almagambetov ACasares MVelipasalar S(2012)Autonomous tracking of vehicle rear lights and detection of brakes and turn signals2012 IEEE Symposium on Computational Intelligence for Security and Defence Applications10.1109/CISDA.2012.6291543(1-7)Online publication date: Jul-2012
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Schewior GFlatt HDolar CBanz CBlume H(2011)A hardware accelerated configurable ASIP architecture for embedded real-time video-based driver assistance applications2011 International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation10.1109/SAMOS.2011.6045463(209-216)Online publication date: Jul-2011
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Fossati ASchönmann PFua P(2011)Real-time vehicle tracking for driving assistanceMachine Vision and Applications10.1007/s00138-009-0243-622:2(439-448)Online publication date: 1-Mar-2011
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