EURASIP Journal on Embedded Systems

EURASIP Journal on Embedded Systems Cover Image
Research Article
Open Access

Embedded Vehicle Speed Estimation System Using an Asynchronous Temporal Contrast Vision Sensor

  • D Bauer1Email author,
  • AN Belbachir1,
  • N Donath1,
  • G Gritsch1,
  • B Kohn1,
  • M Litzenberger1,
  • C Posch1,
  • P Schön1 and
  • S Schraml1
EURASIP Journal on Embedded Systems20072007:082174

https://doi.org/10.1155/2007/82174

©  Bauer et al. 2007

Received: 28 April 2006

Accepted: 30 October 2006

Published: 4 January 2007

Abstract

This article presents an embedded multilane traffic data acquisition system based on an asynchronous temporal contrast vision sensor, and algorithms for vehicle speed estimation developed to make efficient use of the asynchronous high-precision timing information delivered by this sensor. The vision sensor features high temporal resolution with a latency of less than 100 μ s, wide dynamic range of 120 dB of illumination, and zero-redundancy, asynchronous data output. For data collection, processing and interfacing, a low-cost digital signal processor is used. The speed of the detected vehicles is calculated from the vision sensor's asynchronous temporal contrast event data. We present three different algorithms for velocity estimation and evaluate their accuracy by means of calibrated reference measurements. The error of the speed estimation of all algorithms is near zero mean and has a standard deviation better than 3% for both traffic flow directions. The results and the accuracy limitations as well as the combined use of the algorithms in the system are discussed.

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Authors’ Affiliations

(1)
Austrian Research Centers GmbH - ARC

References

  1. Bandoi M, Hasebe K, Nakanishi K, Nakayama A, Shibata A, Sugiyama Y: Phenomenological study of dynamical model of traffic flow. Journal de Physique I 1995,5(11):1389-1399. 10.1051/jp1:1995206View ArticleGoogle Scholar
  2. Pei Y, Lang Y: Research on dynamic traffic assignment applied in traffic congestion analyze and management. Proceedings of IEEE Intelligent Transportation Systems, October 2003, Shanghai, China 2: 1032-1035.Google Scholar
  3. Lam JK, Kerr J, Korpal P, Rayman C: Development of compass - an advanced traffic management system. Proceedings of the IEEE-IEE Vehicle Navigation and Information Systems Conference (VNIS '93), October 1993, Ottawa, Canada 200-203.View ArticleGoogle Scholar
  4. Coifman B, Beymer D, McLauchlan P, Malik J: A real-time computer vision system for vehicle tracking and traffic surveillance. Transportation Research Part C 1998,6(4):271-288. 10.1016/S0968-090X(98)00019-9View ArticleGoogle Scholar
  5. Cathey FW, Dailey DJ: A novel technique to dynamically measure vehicle speed using uncalibrated roadway cameras. Proceedings of IEEE Intelligent Vehicles Symposium, June 2005, Las Vegas, Nev, USA 777-782.Google Scholar
  6. Grammatikopoulos L, Karras G, Petsa E: Automatic estimation of vehicle speed from uncalibrated video sequences. Proceedings of International Symposium on Modern Technologies, Education and Profeesional Practice in Geodesy and Related Fields, November 2005, Sofia, Bulgaria 332-338.Google Scholar
  7. Schoepflin TN, Dailey DJ: Dynamic camera calibration of roadside traffic management cameras for vehicle speed estimation. IEEE Transactions on Intelligent Transportation Systems 2003,4(2):90-98. 10.1109/TITS.2003.821213View ArticleGoogle Scholar
  8. Bramberger M, Brunner J, Rinner B, Schwabach H: Real-time video analysis on an embedded smart camera for traffic surveillance. Proceedings of the 10th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS '04), May 2004, Toronto, Canada 10: 174-181.Google Scholar
  9. Kastrinaki V, Zervakis M, Kalaitzakis K: A survey of video processing techniques for traffic applications. Image and Vision Computing 2003,21(4):359-381. 10.1016/S0262-8856(03)00004-0View ArticleGoogle Scholar
  10. Lichtsteiner P, Posch C, Delbruck T: A 128x128 120dB 30mW asynchronous vision sensor that responds to relative intensity change. Proceedings of IEEE International Solid-State Circuits Conference (ISSCC '06), February 2006, San Francisco, Calif, USA Google Scholar
  11. Lichtsteiner P, Delbruck T: 64x64 event-driven logarithmic temporal derivative silicon retina. Proceedings of IEEE Workshop on Charge-Coupled Devices and Advanced Image Sensors, June 2005, Nagano, Japan 157-160.Google Scholar
  12. Boahen KA: Point-to-point connectivity between neuromorphic chips using address events. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 2000,47(5):416-434. 10.1109/82.842110View ArticleMATHGoogle Scholar
  13. Delbruck T, Lichtsteiner P: Fully programmable bias current generator with 24 bit resolution per bias. Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS '06), May 2004, Kos, Greece 4.Google Scholar
  14. Smart-eye traffic data sensor, http://www.smart-systems.at/products/products_video_tds_en.html
  15. Bauer D, Bühler P, Donath N, et al.: Embedded vehicle counting system with 'silicon retina' optical sensor. Workshop on Information Optics (WIO '06), June 2006, Toledo, Spain Google Scholar
  16. Austrian patent application no. A 1011/2005, June 2005Google Scholar

Copyright

© Bauer et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.