Title page for 975202050


[Back to Results | New Search]

Student Number 975202050
Author Shao-zong Ma(馬紹宗)
Author's Email Address No Public.
Statistics This thesis had been viewed 690 times. Download 467 times.
Department Computer Science and Information Engineering
Year 2009
Semester 2
Degree Master
Type of Document Master's Thesis
Language English
Title Blind-spot Vehicle Detection with Dynamic and Static
Vision Methods
Date of Defense 2010-07-07
Page Count 86
Keyword
  • blind-spot detection
  • ITS
  • optical flow
  • Abstract Developing a real-time automotive driver assistant system for safety has emerged wide attention in recent years. When driving on the road, the fields of view beside the host vehicle for drivers are limited. If the driver changes lane without being aware of the objects in the blind-spot area, the potential collision accident may occur. For ensuring the safety of changing lane, our method uses a camera mounted in side-view mirror to capture the image in blind-spot area and detects the vehicle with computer-vision technology.
    The proposed method offers the blind-spot detection includes defining the detection and decision zone, estimating optical flow, filtering and grouping these estimated optical flow and using the process of tracking and stabilization to accomplish the detection. Considering the situation that optical flow disappears in consecutive tracking process, the proposed method detects the vehicle shadow to keep detecting and tracking. The proposed method also uses the shadow to enhance the detection result generated by optical flow.
    We apply the proposed detection method to many different situations. In experiments, the detection rate in urban area in daylight is about 95%. The detection rate in suburban area is about 97%. The detection rate in night is
    about 90%. The detection method operates in Intel? Core 2 Duo? E8400 3.0 GHz CPU, 2GB DDR RAM, Microsoft? Windows 7 has at least 30 frames per seconds.
    Table of Content 摘要 ∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙ II
    誌謝 ∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙ III
    目錄 ∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙ IV
    第一章 緒論 ∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙ 一
    第二章 相關研究 ∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙ 二
    第三章 光流估計 ∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙ 三
    第四章 車輛偵測 ∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙ 四
    第五章 實驗 ∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙ 五
    第六章 結論及未來工作 ∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙ 六
    附錄 英文版論文 ∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙ 七
    Abstract ............................................................................................................. ii
    Contents ............................................................................................................ iii
    Chapter 1 Introduction ...................................................................................... 1
    1.1 Motivation ........................................................................................ 1
    1.2 System overview .............................................................................. 2
    1.3 Thesis organization ........................................................................... 3
    Chapter 2 Related Works ................................................................................... 5
    2.1 Feature-based detection methods ...................................................... 5
    2.1 Optical flow-based detection method ............................................. 10
    2.1 Sensor-based detection methods ..................................................... 18
    Chapter 3 Optical Flow Estimation ................................................................. 20
    3.1 Definition of optical flow and image flow ...................................... 20
    3.2 Optical-flow estimation................................................................... 21
    3.2.1 Horn and Schunck approach ..................................................... 21
    3.2.2 Lucas and Kanade approach ..................................................... 25
    3.2.1 Pyramidal structure approach ................................................... 27
    Chapter 4 Vehicle Detection ............................................................................ 30
    4.1 Definition of detection and decision region .................................... 30
    4.2 Feature extraction ............................................................................ 33
    4.3 Preprocessing of optical flow .......................................................... 34
    4.3.1 Filtering optical flow ................................................................ 34
    4.3.2 Grouping optical flow .............................................................. 36
    4.4 Lateral vehicle detection ................................................................. 40
    4.4.1 Lateral objects hypothesis ........................................................ 42
    4.4.2 Lateral objects hypothesis verification ..................................... 43
    4.5 Static-information extraction .......................................................... 48
    Chapter 5 Experiments .................................................................................... 51
    5.1 Experimental environments ............................................................ 51
    5.2 Experimental results ........................................................................ 53
    5.3 Discussion ....................................................................................... 58
    Chapter 6 Conclusion and Future Works ........................................................ 59
    6.1 Conclusions ..................................................................................... 59
    6.2 Future works.................................................................................... 60
    Reference [1] Achler, O. and M. M. Trivedi, "Vehicle wheel detector using 2D filter
    banks," in Proc. IEEE Intelligence Vehicles Symp., Parma, Italy,
    Jun.14-17, 2004, pp.25-30.
    [2] Alonso J. D., E. R. Vidal, A. Rotter, and M. Mühlenberg, "Lane-change
    decision aid system based motion-driven vehicle tracking," IEEE Trans.
    Intelligent Transportation Systems, vol.9, pp.185-190, 2008.
    [3] Anandan, P., "A computational framework and an algorithm for the
    measurement of visual motion," Int. Jour. of computer Vision, vol.2, no.3,
    pp. 283-310, 2004.
    [4] Baker, S. and I. Matthew, "Lucas-Kanade 20 years on: A unifying
    framework," Int. Jour. of Computer Vision, vol.56, no.3, pp.221-255,
    2003.
    [5] Barron, J. L., D. J. Fleet, and S. S. Beauchemin, "Performance of optical
    flow techniques," Int. Jour. of Computer Vision, vol.12, no.1, pp.43-77,
    1994.
    [6] Batavia, P. H., D. A. Pomerleau, and C. E. Thorpe, "Overtaking vehicle
    detection using implicit optical flow," in Proc. IEEE Conf. on Intelligent
    Transportation System, Pittsburgh, PA, Nov.9-12, 1997, pp.729-734.
    [7] Becker, L. P., A. Debski, D. Degenhardt, M. Hillenkamp, and I.
    Hoffmann, "Development of a camera-based blind spot information
    system," in Advanced Microsystems for Automotive Applications, J.
    Valldorf and W. Gessner, eds., Springer-Verlag, Berlin, 2005, Ch.6,
    pp.71-84.
    [8] Bishop, R., Intelligent Vehicle Technology and Trends, Artech-House,
    Norwood, 2005.
    - 62 -
    [9] Blanc, N., B. Steux, and T. Hinz, "LaRASideCam - a fast and robust
    vision-based blindspot detection system," in Proc. IEEE Intelligent
    Vehicles Symp., Istanbul, Turkey, Jun.13-15, 2007, pp.480-485.
    [10] Bouguet, J. Y., Pyramidal Implementation of the Lucas Kanade Feature
    Tracker Description of the algorithm, OpenCV technical Document,
    Intel Microprocessor Research Labs, 2007.
    [11] Chung, E. Y., H. C. Jung, E. Chang, and I. S. Lee, "Vision based for lane
    change decision aid system," in Proc. of The 1st Int. Forum on Strategic
    Technology, Ulsan, Korea, Oct.18-20, 2006, pp.10-13.
    [12] David, L. S., Z. Wu, and H. Sun, “Contour-based motion estimation,” in
    Comput. Vision Graphics. Image Proc, vol. 23, Jun, 1982, pp. 313-326.
    [13] Horn, B. K. P. and B. G. Schunck, “Determining optical flow,” Int. Jour.
    of Artificial Intelligence, vol. 17, pp.185–204, 1981.
    [14] Huang, Y.-C., A Vision-based Vehicle to Vehicle Detection and Tracking
    System, Master thesis, Computer Science and Information Engineering
    Dept., National Central Univ., Chungli, Taoyuan, Taiwan, 2005.
    [15] Jin, J.-S., Z. Zhu, and G. Xu, "A stable vision system for moving
    vehicles," IEEE Trans. on Intelligent Transportation Systems, vol.1, no.1,
    pp.32-39, 2000.
    [16] Ko, S.-J., S.-H. Lee, and K.-H. Lee, "Digital image stabilizing algorithm
    based on bit-plane matching," IEEE Trans. on Consumer Electronics,
    vol.44, no.3, pp.617-622, 1998.
    [17] Krips, M., J. Valten, and A. kummert, "AdTM tracking for blind spot
    collision avoidance," in Pro. IEEE Intelligent Vehicles Symp., Parma,
    Italy, Jun. 14-17, 2004, pp.544-548.
    [18] Lin, Y.-H., Visual Blind-spot Detection for Lane Change Assistance,
    - 63 -
    Master thesis, Computer Science and Information Engineering Dept.,
    Univ. of Center, Chungli, Taoyuan, 2009.
    [19] Lucas, B. D. and T. Kanade, "An iterative image registration technique
    with an application to stereo vision," in Proc. 7th Int. Joint Conf. on
    Artificial Intelligence, Vancouver, 1981, pp.674-679.
    [20] Mota, S., E. Ros, J. Díaz, G. Botella, F. Vargas-Martin, and A. Prieto,
    "Motion driven segmentation scheme for car overtaking sequences," in
    Proc. of 10th Int. Conf. on Vision in Vehicles, Granada, Spain, Sept.7-10,
    2003.
    [21] Mota, S., E. Ros, E. M. Ortigosa, and F. J. Pelayo, "Bio-inspired motion
    detection for blind spot overtaking monitor," Int. Jour. of Robotics and
    Automation, vol.19, no.4 pp.190-196, 2004.
    [22] Paik, J. K., Y. C. Park, and D. W. Kim, "An adaptive motion decision
    system for digital image stabilizer based on edge pattern matching,"
    IEEE Trans. on Consumer Electronics, vol.38, no.3, pp.607-616, 1992.
    [23] Ratakonda, K., "Real-time digital video stabilization for multimedia
    applications," in Proc. IEEE Symposium on Circuits and Systems,
    Monterey, CA, May 31-Jun.3, 1998, pp.69-72.
    [24] Reichardt, W., "Autocorrelation, a principle for evaluation of sensory
    information by the central nervous system," in Sensory
    Communication,W. A. Rosenblith, ed., Wiley, New York, 1961,
    pp.303-317.
    [25] Ruder, M., W. Enkelmann, and R. Garnitz, “Highway lane change
    assistant,” in Pro. IEEE Intelligent Vehicles Symp, Versailles, France,
    vol.1, pp.240–144, Jun.17-21, 2002.
    [26] Sotelo, M. A., J. Barriga, D. Fernández, I. Parra, J. E. Naranjo, M.
    Marrón, S. Alvarez, and M. Gavilán, "Vision-based blind spot detection
    - 64 -
    using optical flow," Lecture Notes in Computer Science, vol.4739,
    pp.1113-1118, 2007.
    [27] Uornori, K., A. Morimura, H. Ishii, T. Sakaguchi, and Y. Kitamura,
    "Automatic image stabilizing system by full-digital signal processing,"
    IEEE Trans. on Consumer Electronics, vol.36, no.3, pp.510-519, 1990.
    [28] Wang, J., G. Bebis, and R. Miller, "Overtaking vehicle detection using
    dynamic and quasi-Static background modeling," in Proc. IEEE Conf.
    Computer Vision and Pattern Recognition, San Diego, CA, Jun.20-26,
    2005, pp.64-71.
    [29] Wu, B.-F., W.-H. Chen, C.-W. Chang, and C.-J. Chen, "A new vehicle
    detection with distance estimation for lane change warning systems," in
    Proc. IEEE Intelligent Vehicles Symp., Istanbul, Turkey, Jun.13-15, 2007,
    pp.698-703.
    [30] Zhou, J., D. Gao and D. Zhang, "Moving vehicle detection for automatic
    traffic monitoring," IEEE Trans. of Vehicular Technology, vol.56, no.1,
    pp.51-59, 2007.
    Advisor
  • Din-chang Tseng(曾定章)
  • Files
  • 975202050.pdf
  • approve in 2 years
    Date of Submission 2010-07-28

    [Back to Results | New Search]


    Browse | Search All Available ETDs

    If you have dissertation-related questions, please contact with the NCU library extension service section.
    Our service phone is (03)422-7151 Ext. 57407,E-mail is also welcomed.