Human detection and depth calculation using stereographic vision with pixel disparity and HOG classifier

  • Authors

    • Vemuri Tejasri MTech
    • D. Ravi Babu
    • K. N.V. Satyanarayana
    • D. Srinivas Reddy
    • R. Shiva Shankar
    2019-04-21
    https://doi.org/10.14419/ijet.v7i4.14212
  • Microneurosyms, Stereo Vision, Subpixel Disparity, Depth Calculation.

  • Mainly image processing is used for detection of objects with feasible number of constraints with different detection meth-odologies is defined used by camera-based detection. This method is used to find correspondence with respect to different objects. So that, in this paper, we propose Novel and simple method which is worked based on different region of interests present in video or image. This method mainly worked based on Histogram Oriented Gradients in image processing events. Our method also uses filtering approach with sequential data presentation to access interested data from image or video. Our experimental results mainly show effective visualization results with respect to different selection of regions.

     

     


     

  • References

    1. [1] Weilong Song, Guangming Xiong, Linlang Cao, and Yanhua Jiang, “Depth Calculation and Object Detection Using Stereo Vision with Subpixel Disparity and HOG Feature†Intelligent Vehicle Research Center, Beijing Institute of Technology, Beijing, China,100081.

      [2] “A Fast HOG Descriptor Using Lookup Table and Integral Image†Chunde Huang & Jiaxiang Huang, Xiamen University, 422 Siming S Rd, SimingQu, Xiamen, China, 361005xmuhcd@msn.com.{https://en.wikipedia.org/wiki/Binocular_vision}.

      [3] Gavrila, D.M.: Pedestrian Detection from a Moving Vehicle. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 37–49. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-45053-X_3.

      [4] Ulrich, I., Nourbakhsh, I.: Appearance-Based Obstacle Detection with Monocular Color Vision. In: Proc. of the AAAI National Conference on Artificial Intelligence, Austin, TX (July/August 2000).

      [5] Kalinke, T., Tzomakas, C., von Seelen, W.: A Texture based Object Detection and an Adaptive Model-based Classification. In: Procs. IEEE Intelligent Vehicles Symposium 1998, Stuttgart, Germany, pp. 341–346 (October 1998).

      [6] Kuehnle, A.: Symmetry-based vehicle location for AHS. In: Procs. SPIE Transportation Sensors and Controls: Collision Avoidance, Traffic Management, and ITS, Orlando, FL, vol. 2902, pp. 19–27 (November 1998).

      [7] Nedevschi, S., Danescu, R., Frentiu, D., Marita, T., Oniga, F., Poco, C.: High Accuracy Stereo Vision System for Far Distance Obstacle Detection. In: IEEE Intelligent Vehicles Symposium, June 14-17. University of Parma, Parma (2004).

      [8] Lee, C.-H., Lim, Y.-C., Kwon, S., Lee, J.-H.: Obstacle localization with a binarized v-disparity map using local maximum frequency values in stereo vision. In: International Conference on Signals, Circuits and Systems (2008).

      [9] Bradski, G., Kaehler, A.: Learning OpenCV – Computer Vision with the OpenCV Library. O’Reilly Media, Inc., Sebastopol (2008).

      [10] Bradski, G., Kaehler, A.: Learning OpenCV – Computer Vision with the Open CV Library. O’Reilly Media, Inc., Sebastopol (2008).

      [11] Bouguet, J.-Y.: Visual methods for three-dimensional modeling, PhD Thesis (1999).

      [12] Morgan, G.L.K., Liu, J.G., Yan, H.: Precise Subpixel Disparity Measurement from Very Narrow Baseline Stereo. IEEE Transactions on Geoscience and Remote Sensing 48(9), 3424–3433 (2010). https://doi.org/10.1109/TGRS.2010.2046672.

      [13] Dalal, N.: Finding People in Images and Videos, PhD thesis, France (2006).

      [14] Schmid, C., Mohr, R., Bauckhage, C.: Evaluation of interest point detectors. International Journal of Computer Vision 37(2), 151–172 (2000). https://doi.org/10.1023/A:1008199403446.

  • Downloads

  • How to Cite

    Tejasri, V., Ravi Babu, D., N.V. Satyanarayana, K., Srinivas Reddy, D., & Shiva Shankar, R. (2019). Human detection and depth calculation using stereographic vision with pixel disparity and HOG classifier. International Journal of Engineering & Technology, 7(4), 5732-5735. https://doi.org/10.14419/ijet.v7i4.14212