Color-Driven Object Recognition: A Novel Approach Combining Color Detection and Machine Learning Techniques

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.