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A Computer Vision Based Approach for Object Recognition in Smart Buildings

In: New Trends in Computational Vision and Bio-inspired Computing

Author

Listed:
  • D. Kavin Kumar

    (Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Department of Computer Science and Engineering)

  • Latha Parameswaran

    (Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Department of Computer Science and Engineering)

  • Senthil Kumar Thangavel

    (Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Department of Computer Science and Engineering)

Abstract

Object recognition is one of the essential Computer Vision techniques. The success of object recognition lies in identifying features that strongly represent the object of interest. The manuscript comes up with a hybrid feature descriptor that combines the properties of HOG, ORB and BRISK feature descriptors. Linear SVM is used to classify the feature vectors of the object of interest and other objects in the scene. Occlusion, Orientation and Scaling are some of the limitations in existing approach. From the experimental analysis, we infer that the proposed framework handles partial occlusion and is invariant to scaling and rotation. The framework has been tested with a manually built data library and the classification accuracy of the proposed framework is 0.91, whereas the standalone performance of the HOG, ORB and BRISK are 0.85, 0.87, and 0.89 respectively.

Suggested Citation

  • D. Kavin Kumar & Latha Parameswaran & Senthil Kumar Thangavel, 2020. "A Computer Vision Based Approach for Object Recognition in Smart Buildings," Springer Books, in: S. Smys & Abdullah M. Iliyasu & Robert Bestak & Fuqian Shi (ed.), New Trends in Computational Vision and Bio-inspired Computing, pages 13-22, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-41862-5_2
    DOI: 10.1007/978-3-030-41862-5_2
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