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


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.

[1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16]


  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/jp1:1995206

    Article  Google 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.

    Chapter  Google 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)00019-9

    Article  Google 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.2003.821213

    Article  Google 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)00004-0

    Article  Google 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. 10.1109/82.842110

    Article  MATH  Google 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,

  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 2005

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Correspondence to D Bauer.

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Bauer, D., Belbachir, A., Donath, N. et al. Embedded Vehicle Speed Estimation System Using an Asynchronous Temporal Contrast Vision Sensor. J Embedded Systems 2007, 082174 (2007).

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