Traffic video enhancement based vehicle correct tracked methodology

Traffic video enhancement based vehicle correct tracked methodology

Автор: Mohamed Maher Ata, Mohamed El-Darieby, M.Abd Elnaby, Sameh A. Napoleon

Журнал: International Journal of Image, Graphics and Signal Processing @ijigsp

Статья в выпуске: 12 vol.9, 2017 года.

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In this paper, an enhancement based traffic video has been proposed in the state of the art of computer vision. The main target is to develop a decision making criteria for removing the most probable video degradations. Such traffic video degradations would have an adverse impact on the transportation system. In order to establish the appropriate analysis, three types of video degradations have been added to the test video; salt and pepper noise, Gaussian noise, and speckle noise, we have simulated rainy, fog, and darkness conditions for the traffic video. First of all, back ground subtraction and Kalman filter techniques have been used for detecting and tracking vehicles respectively. By using such algorithms, it would be easily to estimate average number of assigned tracks which express the efficacy of correct detection and prediction of vehicles in each frame. Furthermore, video degradations would be applied in order to studying its effect on the average number of assigned tracks which would be deviated than noiseless video. Spatial filtering system has been applied to state the most suitable filter mask which satisfy the least deviation in the average number of assigned tracks. Experimental results show that median filter satisfies the least deviation in all cases of video degradations.

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Intelligent Transportation System (ITS), traffic video analysis, assigned tracks, video degradation, filter mask

Короткий адрес: https://sciup.org/15015922

IDR: 15015922   |   DOI: 10.5815/ijigsp.2017.12.04

Список литературы Traffic video enhancement based vehicle correct tracked methodology

  • S.Sivaraman, M.M.Trivedi,”looking at vehicles on the road:A survey of vision based vehicle detection, tracking, and behavior analysis”IEEE transaction on intelligent transportation systems, VOL.14,NO.4, 2013.
  • S. Sato, M. Hashimoto, M. Takita, K. Takagi, and T. Ogawa, “Multilayer lidar-based pedestrian tracking in urban environments,” in Proc. IEEE IV, Jun. 2010, pp. 849–854.
  • Z. Kim, “Real time obstacle detection and tracking based on constrained Delaunay triangulation,” in Proc. IEEE ITSC, Sep. 2006, pp. 548–553. J. Nuevo, I. Parra, J. Sjoberg, and L. Bergasa, “Estimating surrounding vehicles’ pose using computer vision,” in Proc. 13th ITSC, Sep. 2010, pp. 1863–1868.
  • C. Idler, R. Schweiger, D. Paulus, M. Mahlisch, and W. Ritter, “Real time vision based multi-target-tracking with particle filters in automotive applications,” in Proc. IEEE Intell. Veh. Symp., 2006, pp. 188–193.
  • J. Nuevo, I. Parra, J. Sjoberg, and L. Bergasa, “Estimating surrounding vehicles’ pose using computer vision,” in Proc. 13th ITSC, Sep. 2010, pp. 1863–1868.
  • M. K. Krishna, H. Hexmoor, and S. Sogani, “A t-stepahead constrained optimal target detection algorithm for a multi sensor surveillance system,” IEEE Intelligent Robots and Systems, pp. 357–362 2005.
  • W.Jing, B.Wen-xing. “A video supported moving object detection technique based on difference algorithm”. Computer Applications and Software, vol.26, no.12, pp: 68-70, 2009.
  • Sun Ling, Wang Shuo, Li Qing-xia. “A target detection method based on adaptive threshold mixed difference”. Computer Applications and Software, vol. 26, no. 10, pp: 94-97, 2009.
  • Xiaopeng Chen, Qiang Huang, et al. “Rapid and Precise Object Detection based on Color Histograms and Adaptive Bandwidth Mean Shift”. International Conference on Intelligent Robots and Systems,IROS 2009. IEEE/RSJ, pp: 4281-4286, 2009.
  • R.Cucchiara, M.Piccardi, A.Prati, “Detecting Moving Objects, Ghosts, and Shadows in Video Streams” , IEEE transactions on pattern analysis and machine intelligence, , october 2003 , vol. 25, no.10.
  • O. Barnich and M. Van Droogenbroeck. Vibe,”A universal background subtraction algorithm for video sequences”, IEEE Trans. Image Process., 20(6):1709–1724, June 2011
  • J.-M. Guo, C.-H. Hsia, Y.-F. Liu, M.-H. Shih, C.-H. Chang, and J.-Y. Wu. “Fast background subtraction based on a multilayer codebook model for moving object detection”, IEEE Trans. Circuits Syst. Video Technol., 23(10):1809–1821, Oct. 2013
  • T. Bouwmans. “Traditional and recent approaches in background modeling for foreground detection”, An overview. Computer Science Review, 1112(0):31–66,2014.
  • L.Xue , C.Jiang , H.Chang , Y.Yang,W.Qin , W.Yuan , A novel Kalman filter for combining outputs of MEMS gyroscope array , elsevier, 4 January 2012 ,page(s)[745 -746].
  • J.Guo , B.M.Williams , W.Huang , Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty q uantification , Tranportation Research Part C: Emerging Technologies , Volume 43, Part 1 , June 2014 , page(s):50 & 51
  • L.Xue , C.Jiang , H.Chang , Y.Yang,W.Qin , W.Yuan , A novel Kalman filter for combining outputs of MEMS gyroscope array , elsevier, 4 January 2012 ,page(s)[745 -746].
  • C N .Ko , C M .Lee , Short-term load forecasting using SVR (support vector regression)-based radial basis function neural network with dual extended Kalman filter , elsevier, 14 December 2012, page(s)[413].
  • M. Alam, J. Ferreira, J. Fonseca, “Introduction to Intelligent Transportation Systems”, jpournal of intelligent transportation system. Studies in Systems, Decision and Control, vol 52. Pp.1-17, 2016. Springer
  • X. Wang; R. Jiang; L. Li; Y. Lin; X. Zheng; F. Y. Wang, "Capturing Car-Following Behaviors by Deep Learning," in IEEE Transactions on Intelligent Transportation Systems, vol.99, 2017, pp.1-11
  • J. K. Suhr; H. G. Jung, "Rearview Camera-Based Back-over Warning System Exploiting a Combination of Pose-Specific Pedestrian Recognitions," in IEEE Transactions on Intelligent Transportation Systems, vol.99, 2017, pp.1 7.
  • S. Kim, B. Coifman,” Assessing the performance of Speed-Info radar traffic sensors”, Journal of Intelligent Transportation Systems.pp.179-189, vol.21, 2017.
  • M. Cobo, A. López-Herrera, F. Herrera, and E. Herrera-Viedma, “A note on the ITS topic evolution in the period 2000–2009 at T-ITS,” IEEE Trans. Intelligent Transportation System, vol. 13, no. 1, pp. 413–420, Mar. 2012.
  • D. Poobathy, R. Manicka Chezian,"Edge Detection Operators: Peak Signal to Noise Ratio Based Comparison", IJIGSP, vol.6, no.10, pp.55-61, 2014.DOI: 10.5815/ijigsp.2014.
  • N. Kulkarni," Color thresholding method for image segmentation of natural images", IJIGSP, vol.4, issue.1, pages 28, 2012. Comparative analysis of automatic vehicle classification techniques: a survey", IJIGSP, vol.4, issue.9, pages 52, 2012.
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