Categorization of Vehicle and Motion Analysis Using Vehicle Features

  • Authors

    • Ms S.Vanithamai
    • Dr S.Purushothaman
    2018-07-15
    https://doi.org/10.14419/ijet.v7i3.10.15656
  • Vehicle detection, Tracking, Motion Analysi

  • This research work can identify the vehicle and classify the vehicle using the vehicle features such as shape, color etc., The parameters extracted from the vehicle classification are based on movement of the vehicle are classified as static, movement variation in the successive video frames are used to identify the hazardness of the vehicle. Digital Image processing techniques are used in the object detection.

     

  • References

    1. [1] Zielke T., Brauckmann M., and Von Seelen W., “Intensity and Edge – Based symmetry detection with an application to car – following†Image Understanding, 58,2,177-190,1993

      [2] Koller D., Daniilidis K., and Nagel H.H., “Model – based Object tracking in Monocular image sequences of Road Traffic Scenecâ€, International journal of Computer Vision,10,3,257-281,1993

      [3] G.Zhao and Y.Shinichi, Obstacle detection by vision system for autonomous vehicle, IEEE Intelligent Vehicle Symposium, pp. 31-36, 1993.

      [4] N.M. Charkari, H.Mori, Visual vehicle detection and tracking based on the sign pattern , Advanced Robotics, Vol 9, No 4, pp. 367-382, 1995.

      [5] M.Betke, E.Haritaglu and L.Davis, Multiple vehicle detection and tracking in hard real time, IEEE Intelligent Vehicles Symposium, pp. 351-356, 1996.

      [6] M.Betke, E.Haritaglu and L.Davis, Multiple vehicle detection and tracking in hard real time, IEEE Intelligent Vehicles Symposium, pp. 351–356, 1996.

      [7] N.Matthews, P.An, D.Charnley, and C.Harris, Vehicle detection and recognition in greyscale imagery, Control Engineering Practice, vol. 4, pp. 473–479, 1996.

      [8] N.Friedman and S.Russell, Image segmentation in video sequences: a probabilistic approach, Proc. Thirteenth Conf. on Uncertainty in Artificial Intelligence (UAI 97), 1997.

      [9] A.N.Rajagopalan, Philippe Burlina and R.Chellappa, Higher order statistical learning for vehicle detection in images ,Proc. IEEE Intl. Conf. on Computer Vision, (Corfu, Greece), 1999, pp. 1204-1209.

      [10] M.Betke, E.Haritaoglu and L.S.Davis, Real-time multiple vehicle detection and tracking from a moving vehicle , Machine Vision and Applications, Vol.12, pp. 69-83, 2000.

      [11] M.Suwa, A stereo-based vehicle detection method under windy conditions, IEEE Intelligent Vehicle Symposium, pp. 246-249, 2000.

      [12] Z.Sun, G.Bebis, and R.Miller, Improving the performance of on-road vehicle detection by combining gabor and wavelet features, The IEEE Fifth International Conference on Intelligent Transportation Systems ,September, 2002, Singapore.

      [13] B.Leibe, A.Leonardis, and B.Schiele. Combined object categorization and segmentation with an implicit shape model .In ECCV’04 Works. On Stats Learning in Comp. Vision, pages 17-32, May 2004.

      [14] A.N.Rajagopalan, and R.Chellappa. Vehicle detection and tracking in video. Center for Automation Research, Univerisyt of MaryLand, College Park, MD 20742 -3275

  • Downloads

  • How to Cite

    S.Vanithamai, M., & S.Purushothaman, D. (2018). Categorization of Vehicle and Motion Analysis Using Vehicle Features. International Journal of Engineering & Technology, 7(3.10), 184-186. https://doi.org/10.14419/ijet.v7i3.10.15656