Object Detection and Tracking using Multiple Features Extraction
Bhavya. R1, Geetha K S2

1Bhavya Rudraiah*, Research Scholar, Department of ECE, R V College of Engineering, Bangalore, India.
2Dr. Geetha K. S., Professor, HOD, Department of ECE, R V College of Engineering, Bangalore, India.
Manuscript received on August 16, 2021. | Revised Manuscript received on August 23, 2021. | Manuscript published on August 30, 2021. | PP: 75-79 | Volume-10, Issue-10, August 2021 | Retrieval Number: 100.1/ijitee.J943708101021 | DOI: 10.35940/ijitee.J9437.08101021
Open Access | Ethics and  Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

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: Decision Tree, Edges, Histogram of oriented Gradients, Locality Sensitive Histogram, Support Vector machine.