Multiple Detection and Tracking of Multi Class Vehicles using Locality Sensitive Histogram
Bhavya R1, Geetha K S2
1Bhavya Rudraiah*, Research Scholar, Dept. of ECE, R V College of Engineering, Bangalore, India.
2Dr. Geetha K S, Professor, HOD, Dept. of ECE, R V College of Engineering, Bangalore, India.
Manuscript received on January 20, 2021. | Revised Manuscript received on January 26, 2021. | Manuscript published on January 30, 2021. | PP: 259-262 | Volume-9 Issue-5, January 2021. | Retrieval Number: 100.1/ijrte.E5295019521 | DOI: 10.35940/ijrte.E5295.019521
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© 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: Multiple object detection and tracking in a cluttered background is most important in vision-based applications. In this paper, the goal is to develop a classifier that detects and tracks multiple objects thereby ensuring robustness and accuracy. Locality Sensitive Histogram feature extraction is used, which adds contributions from all the pixels in an image. These features extracted are trained using decision tree classifier which performs with an accuracy of 97%. Experimental results demonstrate the objects tracked and detected under different scale and pose variations. Evaluation and comparison of the proposed method with various other techniques is performed using performance parameters. Results depict that the proposed technique outperforms with increased accuracy and is the top performer.
Keywords: Decision tree, Detection, Locality Sensitive Histogram, Tracking.