Editorial
Color-Driven Object Recognition: A Novel Approach Combining Color Detection and Machine Learning Techniques
@ARTICLE{10.4108/eetiot.5495, author={Aadarsh Nayyer and Abhinav Kumar and Aayush Rajput and Shruti Patil and Pooja Kamat and Shivali Wagle and Tanupriya Choudhury}, title={Color-Driven Object Recognition: A Novel Approach Combining Color Detection and Machine Learning Techniques}, journal={EAI Endorsed Transactions on Internet of Things}, volume={10}, number={1}, publisher={EAI}, journal_a={IOT}, year={2024}, month={3}, keywords={You only look once, YOLO, Red Gree Blue, RGB values, K-means Algotithm}, doi={10.4108/eetiot.5495} }- Aadarsh Nayyer
Abhinav Kumar
Aayush Rajput
Shruti Patil
Pooja Kamat
Shivali Wagle
Tanupriya Choudhury
Year: 2024
Color-Driven Object Recognition: A Novel Approach Combining Color Detection and Machine Learning Techniques
IOT
EAI
DOI: 10.4108/eetiot.5495
Abstract
INTRODUCTION: Object recognition is a crucial task in computer vision, with applications in robotics, autonomous vehicles, and security systems. OBJECTIVES: The objective of this paper is to propose a novel approach for object recognition by combining color detection and machine learning techniques. METHODS: The research employs YOLO v3, a state-of-the-art object detection algorithm, and k-means optimized clustering to enhance the accuracy and efficiency of object recognition. RESULTS: The main results obtained in this paper showcase the outperformance of the authors’ approach on a standard object recognition dataset compared to state-of-the-art approaches using only color features. Additionally, the effectiveness of this approach is demonstrated in a real-world scenario of detecting and tracking objects in a video stream. CONCLUSION: In conclusion, this approach, integrating color and shape features, has the potential to significantly enhance the accuracy and robustness of object recognition systems. This contribution can pave the way for the development of more reliable and efficient object recognition systems across various applications.
Copyright © 2024 A. Nayyer et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
