Object Detection and Tracking using Multiple Features Extraction
- 30 August 2021
- journal article
- Published by Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP in International Journal of Innovative Technology and Exploring Engineering
- Vol. 10 (10), 75-79
- https://doi.org/10.35940/ijitee.j9437.08101021
Abstract
In most of the video analysis applications, object detection and tracking play vital role. Most of detection and tracking algorithms fail to predict multiple objects with varying orientation. In this paper, the goal is to identify and track multiple objects using different feature extraction methods like Locality Sensitive Histogram, Histogram of Oriented Gradients and Edges. These features are subjected to train classifier that can detect the object of different orientations. Experimental results and performance evaluation depicts the proposed method which uses LSH performs well with an increased accuracy of 98%. This method can precisely track the object and can be utilized to track under different scale and pose variations.Keywords
This publication has 3 references indexed in Scilit:
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- Histogram of Oriented Gradients Feature Extraction From Raw Bayer Pattern ImagesIEEE Transactions on Circuits and Systems II: Express Briefs, 2020
- Robust Object Tracking via Locality Sensitive HistogramsIEEE Transactions on Circuits and Systems for Video Technology, 2016