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Robust Vehicle Detection and Distance Estimation Under Challenging Lighting Conditions 원문보기

IEEE transactions on intelligent transportation systems : a publication of the IEEE Intelligent Transportation Systems Council, v.16 no.5, 2015년, pp.2723 - 2743  

Rezaei, Mahdi (Fac. of Comput. & Inf. Technol. Eng., Islamic Azad Univ., Qazvin, Iran) ,  Terauchi, Mutsuhiro (Dept. of Psychol., Hiroshima Int. Univ., Hiroshima, Japan) ,  Klette, Reinhard (Dept. of Electr. & Comput. Eng., Auckland Univ. of Technol., Auckland, New Zealand)

Abstract AI-Helper 아이콘AI-Helper

Avoiding high computational costs and calibration issues involved in stereo-vision-based algorithms, this paper proposes real-time monocular-vision-based techniques for simultaneous vehicle detection and inter-vehicle distance estimation, in which the performance and robustness of the system remain ...

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참고문헌 (66)

  1. 10.1109/CVPR.2001.990517 

  2. Rezaei, M., Klette, R.. Novel Adaptive Eye Detection and Tracking for Challenging Lighting Conditions. Lecture notes in computer science, vol.7729, 427-440.

  3. 10.1109/CVPR.2005.177 

  4. Felzenszwalb, P F, Girshick, R B, McAllester, D, Ramanan, D. Object Detection with Discriminatively Trained Part-Based Models. IEEE transactions on pattern analysis and machine intelligence, vol.32, no.9, 1627-1645.

  5. 10.1109/CVPR.2010.5539906 

  6. Vargas, M, Milla, J M, Toral, S L, Barrero, F. An Enhanced Background Estimation Algorithm for Vehicle Detection in Urban Traffic Scenes. IEEE transactions on vehicular technology, vol.59, no.8, 3694-3709.

  7. Rezaei, M., Terauchi, M.. Vehicle Detection Based on Multi-feature Clues and Dempster-Shafer Fusion Theory. Lecture notes in computer science, vol.8333, 60-72.

  8. Rezaei, M., Nafchi, H.Z., Morales, S.. Global Haar-Like Features: A New Extension of Classic Haar Features for Efficient Face Detection in Noisy Images. Lecture notes in computer science, vol.8333, 302-313.

  9. Jazayeri, A, Hongyuan Cai, Jiang Yu Zheng, Tuceryan, Mihran. Vehicle Detection and Tracking in Car Video Based on Motion Model. IEEE transactions on intelligent transportation systems : a publication of the IEEE Intelligent Transportation Systems Council, vol.12, no.2, 583-595.

  10. 10.1109/IVS.2012.6232119 

  11. 10.1109/CVPR.2012.6248074 

  12. An introduction to Bayesian and Dempster–Shafer data fusion koks 2005 

  13. A Mathematical Theory of Evidence shafer 1976 10.1515/9780691214696 

  14. Proc Comput Anal Images Patterns New lane model and distance transform for lane detection and tracking jiang 0 10.1007/978-3-642-03767-2_127 5702 1044 

  15. 10.1109/IVS.2009.5164288 

  16. ZuWhan Kim. Robust Lane Detection and Tracking in Challenging Scenarios. IEEE transactions on intelligent transportation systems : a publication of the IEEE Intelligent Transportation Systems Council, vol.9, no.1, 16-26.

  17. Proc Int Symp Veh Comput Syst Vehicle detection at night based on taillight detection o'malley 0 128 

  18. Proc Signals Syst Distance determination for an automotive environment using inverse perspective mapping in OpenCV tuohy 0 100 

  19. iROADS Dataset (Intercity Roads and Adverse Driving Scenarios) Enpeda Image Sequence Analysis Test Site-EISATS Set 10 rezaei 2014 

  20. 10.1109/IVS.2009.5164346 

  21. Multi-Media Modeling Conference International Motion and Haar-like features based vehicle detection bai 0 356 

  22. National Highway Traffic Safety Administration Traffic Safety Facts 2012 

  23. Proc Workshop Planning Perception Navig Intell Veh Real-time visual detection of vehicles and pedestrians with new efficient AdaBoost features moutarde 0 70 

  24. 10.1109/ITSC.2007.4357743 

  25. 10.1109/RIVF.2007.369140 

  26. Klette, R, Kruger, N, Vaudrey, T, Pauwels, K, van Hulle, M, Morales, S, Kandil, F I, Haeusler, R, Pugeault, N, Rabe, C, Lappe, M. Performance of Correspondence Algorithms in Vision-Based Driver Assistance Using an Online Image Sequence Database. IEEE transactions on vehicular technology, vol.60, no.5, 2012-2026.

  27. Haar, Alfred. Zur Theorie der orthogonalen Funktionensysteme : Erste Mitteilung. Mathematische Annalen, vol.69, no.3, 331-371.

  28. 10.1109/WIAMIS.2009.5031451 

  29. 10.1109/CVPR.2014.24 

  30. Proc Pac Rim Symp Adv Image Video Technol A statistical method for peak localization in Hough space by analysing butterflies xu 0 8333 111 

  31. Proc Eur Conf Comput Vis Machine learning for high-speed corner detection rosten 0 430 

  32. Proc Opt Pattern Recog Template matching using fast normalized cross correlation briechle 0 95 

  33. Gu, Hui-Zhen, Lee, Suh-Yin. Car model recognition by utilizing symmetric property to overcome severe pose variation. Machine vision and applications, vol.24, no.2, 255-274.

  34. Pattern Recognit An efficient chain code with Huffman coding liu 2005 10.1016/j.patcog.2004.08.017 38 553 

  35. Freeman, Herbert. On the Encoding of Arbitrary Geometric Configurations. IRE transactions on electronic computers, vol.ec10, no.2, 260-268.

  36. O'Malley, Ronan, Jones, Edward, Glavin, Martin. Rear-Lamp Vehicle Detection and Tracking in Low-Exposure Color Video for Night Conditions. IEEE transactions on intelligent transportation systems : a publication of the IEEE Intelligent Transportation Systems Council, vol.11, no.2, 453-462.

  37. Proc IEEE Comput Vis Pattern Recog Good features to track shi 0 593 

  38. 10.1109/CVPR.2012.6247715 

  39. 10.1109/ICCV.2011.6126544 

  40. Proc Eur Signal Process Conf Radar-vision fusion for vehicle detection by means of improved Haar-like feature and AdaBoost approach haselhoff 0 2070 

  41. White Paper XA Spartan-6 Automotive FPGAs XILINX WP399 (v1 0) Automotive driver assistance systems: Using the processing power of FPGAs zoratti 2011 

  42. Rezaei, Mahdi, Klette, Reinhard. Simultaneous analysis of driver behaviour and road condition for driver distraction detection. International journal of image and data fusion, vol.2, no.3, 217-236.

  43. Concise Computer Vision klette 2014 10.1007/978-1-4471-6320-6 

  44. 10.1109/ICICT.2005.1598621 

  45. Proc World Acad Sci Eng Technol Vehicle detection method using Haar-like features on real time system han 0 455 

  46. Toulminet, Bertozzi, Mousset, Bensrhair, Broggi. Vehicle detection by means of stereo vision-based obstacles features extraction and monocular pattern analysis. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, vol.15, no.8, 2364-2375.

  47. Proc Asian Conf Comput Vision Iterative semi-global matching for robust driver assistance systems hermann 0 7726 465 

  48. Realtime On-Road Vehicle Detection with Optical Flows and Haar-like Feature Detector choi 2006 

  49. Ojala, T., Pietikainen, M., Maenpaa, T.. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE transactions on pattern analysis and machine intelligence, vol.24, no.7, 971-987.

  50. Qian, Zhi Ming, Shi, Hong Xing, Yang, Jia Kuan. Video Vehicle Detection Based on Local Feature. Advanced materials research : AMR, vol.186, 56-60.

  51. 10.1109/VETECS.2012.6239921 

  52. Sun, Zehang, Bebis, G., Miller, R.. On-road vehicle detection: a review. IEEE transactions on pattern analysis and machine intelligence, vol.28, no.5, 694-711.

  53. 10.1109/ITSC.2007.4357637 

  54. Heidelberg Vision Challenge at ECCV 2012 

  55. Distronic Plus With Steering Assist 2013 

  56. Wei Yao, Stilla, U.. Comparison of Two Methods for Vehicle Extraction From Airborne LiDAR Data Toward Motion Analysis. IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society, vol.8, no.4, 607-611.

  57. Kiryati, N., Eldar, Y., Bruckstein, A.M.. A probabilistic Hough transform. Pattern recognition, vol.24, no.4, 303-316.

  58. 10.1109/IVS.2012.6232199 

  59. KITTI Benchmark Website CAR Detection Benchmark 2013 

  60. Computer Vision Laboratory EPFL Multi-View Car Dataset 2012 

  61. Duda, Richard O., Hart, Peter E.. Use of the Hough transformation to detect lines and curves in pictures. Communications of the ACM, vol.15, no.1, 11-15.

  62. Matas, J., Galambos, C., Kittler, J.. Robust Detection of Lines Using the Progressive Probabilistic Hough Transform. Computer vision and image understanding : CVIU, vol.78, no.1, 119-137.

  63. Bicubic Interpolation 2013 

  64. Crow, Franklin C.. Summed-area tables for texture mapping. Computer graphics, vol.18, no.3, 207-212.

  65. Papageorgiou, Constantine; Poggio, Tomaso etc. "A Trainable System for Object Detection." International journal of computer vision, v.38 no.1 (2000), pp. 15-33, doi:10.1023/A:1008162616689.

  66. Caltech Vehicle Dataset philip 2001 

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