IJMECS Vol. 8, No. 8, 8 Aug. 2016
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Vision Sensor, Image segmentation, Machine vision, Object recognition
Detection and tracking of stable features in moving real time video sequences is one of the challenging task in vision science. Vision sensors are gaining importance due to its advantage of providing much information as compared to recent sensors such as laser, infrared, etc. for the design of real–time applications. In this paper, a novel method is proposed to obtain the features in the moving vehicles in outdoor scenes and the proposed method can track the moving vehicles with improved matched features which are stable during the span of time. Various experiments are conducted and the results show that features classification rates are higher and the proposed technique is compared with recent methods which show better detection performance.
Kajal Sharma, "Optimization and Tracking of Vehicle Stable Features Using Vision Sensor in Outdoor Scenario", International Journal of Modern Education and Computer Science(IJMECS), Vol.8, No.8, pp.36-42, 2016. DOI:10.5815/ijmecs.2016.08.05
[1]R. Manduchi, A. Castano, A. Talukder, and L. Matthies, "Obstacle detection and terrain classification for autonomous off-road navigation," Autonomous Robots, vol. 18, no. 1, pp. 81–102, Jan. 2005.
[2]E. Zamora and W. Yu, "Recent advances on simultaneous localization and mapping for mobile robots," IETE Technical Review, vol. 30, no. 6, pp. 490-496, Nov.-Dec. 2013.
[3]Gargi, S. Dahiya,"A Gaussian Filter based SVM Approach for Vehicle Class Identification", IJMECS, vol.7, no.12, pp.9-16, 2015.
[4]S. Avidan, "Support vector tracking," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 26, no. 8, pp. 1064–1072, Aug. 2004.
[5]O. Williams, A. Blake, and R. Cipolla, "Sparse bayesian learning for efficient visual tracking, " IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 27, no. 8, pp. 1292–1304, Aug. 2005.
[6]V. Lepetit, P. Lagger, and P. Fua, "Randomized trees for real-time keypoint recognition," in Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, pp. 775–781, June 2005.
[7]M. Ozuysal, V. Lepetit, F. Fleuret, and P. Fua, "Feature harvesting for tracking-by-detection,” in Proc. 9th European Conf. on Computer Vision, Graz, Austria, pp. 592–605, May 2006.
[8]M. Isard, and J. MacCormick, "Bramble: a bayesian multiple-blob tracker," in Proc. IEEE Int. Conf. on Computer Vision, Vancouver, BC, pp. 34–41, July 2001.
[9]X. Liu, and T. Yu, "Gradient feature selection for online boosting," in Proc. IEEE Int. Conf. on Computer Vision, Rio de Janeiro, pp. 1–8, Oct. 2007.
[10]H. Grabner, and H. Bischof, "On-line boosting and vision," in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 260–267, June 2006.
[11]G. Monteiro, J. Marcos, M. Ribeiro, and J. Batista, "Robust segmentation for outdoor traffic surveillance," in Proc. 15th IEEE Int. Conf. on Image Processing, pp. 2652-2655, Oct. 2008.
[12]W. Wang, J. Yang, and W. Gao, "Modeling background and segmenting moving objects from compressed video," IEEE Transactions on Circuits and Systems for Video Technology, vol. 18, no. 5, pp. 670-681, May 2008.
[13]T. Yoshida, S. Mohottala, M. Kagesawa, and K. Ikeuchi, “Vehicle classification system with local-feature based algorithm using CG model images,” IEICE Trans. on Information and Systems, vol.E85--D, no. 11, pp. 1745-1752, 2002.
[14]D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. of Computer Vision, vol. 60, no. 2, pp. 91–110, Jan. 2004.
[15]K. Mikolajczyk, and C. Schmid, “Scale & affine invariant interest point detectors,” Int. J. of Computer Vision, vol. 60, no.1, pp. 63–86, Oct. 2004.
[16]H. Bay, T. Tuytelaars, and L. V. Goo, “SURF: speeded up robust features,” Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346–359, June 2008.
[17]M. A. Akinlar, M. Kurulay, A. Secer, and M. Celenk, “A novel matching of MR images using gabor wavelets,” IETE Technical Review, vol. 30, no. 2, pp. 129-133, Jan.-Feb. 2013.
[18]W. Chi-Chen Raxle, and J. J. J. Lien, “Automatic vehicle detection using local features—A statistical approach,”, IEEE Trans. on Intelligent Transportation Systems, vol. 9, no. 1, pp. 83-96, March 2008.
[19]W. Tao, and Z. Zhigang, “Real time moving vehicle detection and reconstruction for improving classification,” in Proc. IEEE Workshop on Applications of Computer Vision, Breckenridge, CO, pp. 497-502, Jan. 2012.
[20]W. Zhang, Q.M. Jonathan Wu, and H. bing Yin, “Moving vehicles detection based on adaptive motion histogram,” Digit. Signal Process., vol. 20, no. 3, pp. 793-805, May 2010.
[21]M. Xiaoxu, and W. E. L. Grimson, “Edge-based rich representation for vehicle classification,” in Proc. Tenth IEEE Int. Conf. on Computer Vision, Beijing, pp. 1185-1192, Oct. 2005.
[22]I. Gordon, and D. G. Lowe, “Scene modelling, recognition and tracking with invariant image features,” in Proc. Int. Symposium on Mixed and Augmented Reality, Arlington, VA, pp. 110–119, Nov. 2004.
[23]B. Babenko, M. H. Yang, and S. Belongie, "Visual tracking with online multiple instance learning," in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Miami, FL, pp. 983–990, June 2009.
[24]Z. Kalal, J. Matas, and K. Mikolajczyk, "P-N learning: Bootstrapping binary classifiers by structural constraints," in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, San Francisco, CA , pp. 49–56, June 2010.
[25]T. Yoshida, S. Mohottala, M. Kagesawa, and K. Ikeuchi, "Vehicle classification system with local-feature based algorithm using CG model images," IEICE Trans. on Information and Systems, vol.E85--D, no. 11, pp. 1745-1752, 2002.