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Issue No.05 - Sept.-Oct. (2012 vol.27)
pp: 75-80
Kunfeng Wang , Chinese Academy of Sciences
Wuling Huang , Chinese Academy of Sciences
Bin Tian , Chinese Academy of Sciences
Ding Wen , National University of Defense Technology
Because of the technical and cost constraints on traditional measurement methods, there is a lack of long-term driving behavior data from natural traffic scenes, and this situation has been hindering research progress into driving behavior modeling and other related topics. Thanks to high-definition cameras and advanced visual measurement methods, traffic visual detection is entering a new stage of traffic visual measurement, and thus we can expect to achieve accurate segmentation, positioning, and measurement for road vehicles from live video to meet the requirement for field test data in behavior modeling. To measure driving behaviors in a cost-effective manner, the authors propose a comprehensive visual measurement approach that could perform well in complex traffic scenes. Specifically, they describe a procedure for traffic visual measurement, some preliminary algorithms, and some representative experimental results. Comparisons between the proposed method and three traditional ones (driving simulator, in-vehicle data recorder, and remote-sensing camera) indicate that the biggest advantage of the proposed method is it can measure driving behaviors from live video. Hence, the ongoing research will greatly benefit cognition in driving behavior models.
Iintelligent transportation systems, Intelligent vehicles, Costs, Road vehicles, Visualization, Data models, Cameras, Remote sensing, Cognition, live video, computer vision, traffic visual measurement, driving behaviors, vehicle segmentation
Kunfeng Wang, Wuling Huang, Bin Tian, Ding Wen, "Measuring Driving Behaviors from Live Video", IEEE Intelligent Systems, vol.27, no. 5, pp. 75-80, Sept.-Oct. 2012, doi:10.1109/MIS.2012.100
1. L. Li et al., "Cognitive Cars: A New Frontier for ADAS Research," IEEE Trans. Intelligent Transportation Systems, vol. 13, no. 1, 2012, pp. 395–407.
2. T. Toledo, H.N. Koutsopoulos, and M. Ben-Akiva, "Estimation of an Integrated Driving Behavior Model," Transportation Research Part C: Emerging Technologies, vol. 17, no. 4, 2009, pp. 365–380.
3. F.-Y. Wang, "Parallel Control and Management for Intelligent Transportation Systems: Concepts, Architectures, and Applications," IEEE Trans. Intelligent Transportation Systems, vol. 11, no. 3, 2010, pp. 630–638.
4. J. Zhang et al., "Data-Driven Intelligent Transportation Systems: A Survey," IEEE Trans. Intelligent Transportation Systems, vol. 12, no. 4, 2011, pp. 1624–1639.
5. H. Arioui et al., "From Design to Experiments of a 2-DOF Vehicle Driving Simulator," IEEE Trans. Vehicular Technology, vol. 60, no. 2, 2011, pp. 357–368.
6. A. Perez et al., "Argos: An Advanced In-Vehicle Data Recorder on a Massively Sensorized Vehicle for Car Driver Behavior Experimentation," IEEE Trans. Intelligent Transportation Systems, vol. 11, no. 2, 2010, pp. 463–473.
7. J.J. Vinagre Diaz et al., "Extended Floating Car Data System: Experimental Results and Application for a Hybrid Route Level of Service," IEEE Trans. Intelligent Transportation Systems, vol. 13, no. 1, 2012, pp. 25–35.
8. A. Angel et al., "Methods of Analyzing Traffic Imagery Collected from Aerial Platforms," IEEE Trans. Intelligent Transportation Systems, vol. 4, no. 2, 2003, pp. 99–107.
9. Z. Zhang, D. Xu, and M. Tan, "Visual Measurement and Prediction of Ball Trajectory for Table Tennis Robot," IEEE Trans. Instrumentation and Measurement, vol. 59, no. 12, 2010, pp. 3195–3205.
10. N. Buch, S.A. Velastin, and J. Orwell, "A Review of Computer Vision Techniques for the Analysis of Urban Traffic," IEEE Trans. Intelligent Transportation Systems, vol. 12, no. 3, 2011, pp. 920–939.
11. B.T. Morris and M.M. Trivedi, "A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance," IEEE Trans. Circuits and Systems for Video Technology, vol. 18, no. 8, 2008, pp. 1114–1127.
12. V. Punzo, M.T. Borzacchiello, and B. Ciuffo, "On the Assessment of Vehicle Trajectory Data Accuracy and Application to the Next Generation SIMulation (NGSIM) Program Data," Transportation Research Part C: Emerging Technologies, vol. 19, no. 6, 2011, pp. 1243–1262.
13. IMS Research, "Changing Trends in the Video Surveillance Storage Market,"
14. E. Provenzi et al., "A Spatially Variant White-Patch and Gray-World Method for Color Image Enhancement Driven by Local Contrast," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, no. 10, 2008, pp. 1757–1770.
15. S.Z. Li, Markov Random Field Modeling in Image Analysis, 3rd ed. Springer, 2010.
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