Real Time Object Recognition and Classification using Deep Learning

Authors(4) :-Kiruthiga N, Divya E, Haripriya R, Haripriya V.

Navigation in indoor environments is highly challenging for visually impaired person, particularly in spaces visited for the first time. Various solutions have been proposed to deal with this challenge. In this project consider as the real time object Recognition and classification using deep learning algorithms. Object detection mainly deals with identification of real time objects such as people, animals, and objects. Object detection algorithm uses a wide range of image processing applications for extracting the object's desired portion. This enables one to identify the objects and calculate the accuracy of the object and deliver through voice. Using this information, the system determines the user's trajectory and can locate possible obstacles in that route.

Authors and Affiliations

Kiruthiga N
Department of Computer science and Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India
Divya E
Department of Computer science and Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India
Haripriya R
Department of Computer science and Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India
Haripriya V.
Department of Computer science and Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India

Object Detection , Image Processing , Speech recognition , Deep learning, Convolutional neural networks, Background Subtraction.

  1. K. K. Hati, P. K. Sa and B. Majhi, "Intensity vary based mostly Background Subtraction for Effective Object Detection," in IEEE Signal process Letters, vol. 20, no. 8, pp. 759-762, Aug. 2013.
  2. F. Z. Chelali, N. Cherabit and A. Djeradi, "Face recognition system using skin detection in RGB and YCbCr color house," 2015 2d World symposium on web Applications and Networking (WSWAN), Sousse, 2015, pp. 1-7.
  3. P. Liu, "Colour detection and segmentation of the scene based on Gaussian mixture model clustering," 2017 7th IEEE International Conference on Electronics Information and Emergency Communication (ICEIEC), Macau, 2017, pp. 503-506.
  4. International Conference on special effects, Imaging and visual image. IEEE, 2009, pp.317–322.
  5. F.Labrosse, “Short and long-range visual navigation victimisation crooked wide pictures,” artificial intelligence and Autonomous Systems, vol. 55, no. 9, pp. 675–684,2007.
  6. W. Junqiu and Y. Yagi, “Integrating color and shapetexture options for adaptational period object chase,” IEEE Trans on Image process, vol.17,no.2,pp.235–240,2008.
  7. Q. Wang and Z. Gao, “Study on a period Image Object chase System,” in engineering and machine Technology, 2008. ISCSCT’08.InternationalSymposiumon,vol.2,2008.
  8. L. Braun, D.G ”ohringer, T. Perschke, V. Schatz, M. H ”ubner, and J. Becker, “Adaptive period image process of period Image process, vol. 4, no. 2, pp. 109–125, 2009.
  9. Y. Meng, “Agent-based reconfigurable design for period object pursuit,” Journal of period Image process, vol. 4, no. 4, pp. 339–351,2009.
  10. A. J. Lipton, H. Fujiyoshi, and R. S. Patil, “Moving target classification and pursuit from period video,” in Applications of laptop Vision, 1998. WACV’98. Proceedings . Fourth IEEE Workshop on. IEEE, 1998, pp.8–1.

Publication Details

Published in : Volume 5 | Issue 2 | March-April 2019
Date of Publication : 2019-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 355-359
Manuscript Number : CSEIT195262
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Kiruthiga N, Divya E, Haripriya R, Haripriya V., "Real Time Object Recognition and Classification using Deep Learning", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 2, pp.355-359, March-April-2019. Available at doi : https://doi.org/10.32628/CSEIT195262
Journal URL : http://ijsrcseit.com/CSEIT195262

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