A Computer Vision Based Approach for Object Recognition in Smart Buildings

D. Kavin Kumar (), Latha Parameswaran () and Senthil Kumar Thangavel ()
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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

A chapter in New Trends in Computational Vision and Bio-inspired Computing, 2020, pp 13-22 from Springer

Abstract: 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.

Keywords: HOG; AKAZE; ORB; SVM; BRISK; Deep learning (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-41862-5_2

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DOI: 10.1007/978-3-030-41862-5_2

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