© January 2021| IJIRT | Volume 7 Issue 8 | ISSN: 2349-6002 IJIRT 150658 INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN TECHNOLOGY 191 Real-Time object color Identification Atharva Borawake 1 , Nilima Kulkarni 2 , Anshul Ghorse 3 1,2,3 Department of Computer Science and Engineering, MIT School of Engineering, MIT Arts Design and Technology University, Pune,412201, India Abstract - The computer vision field is a rapidly growing field devoted to analyzing and understanding digital images. We can create computer vision projects through OpenCV. In OpenCV image processing processes such as image filtering, simple geometric photo transformation, color space transition, histograms, etc. are covered. Picture and real-time object color identification focus on OpenCV color identification through using the RGB model as well as the K-Nearest Neighbors Classification algorithm trained on r, g, b pixel values. Color identification in the image can be done through the RGB value of the target pixel as input and then calculates the distance, and the nearest color is chosen. From this method, we can identify 800 plus different colors from our datasets including the RGB value of each color. We conduct extraction of features in real-time color identification of objects to extract their RGB color Histogram attributes from training images and trained classification algorithm via RGB Color Histogram attributes. The KNN classifier analyzes the webcam frames and performs feature extraction and then shows the color. Index Terms - Color Histogram, Feature extraction, K- Nearest Neighbor (KNN) 1.INTRODUCTION Software applications & devices try to imitate human eyes. Using libraries like OpenCV we can develop computer vision applications. In OpenCV image processing operations such as image color analysis, color space conversion, histograms, etc. are covered. Digital image identification is a program that helps you to instantly acquire the identity of color by simply clicking on it. Then, from each color, we will measure the distance and determine the shorter one. In Color identification of the real-time object, we can pan the camera towards the target object to identify the color. For this, we trained the KNN classifier on the training dataset which contains color images and can be updated with new colors. Color identification is a technique used in different applications like Photoshop and several other animation software as a color dropper feature. For various AI-based systems or robots color recognition of an object is required. This paper represents an approach based on the K Nearest Neighbor (KNN) classifier and feature extraction to detect the real-time color of an object. The extracted features are being used to get the Color Histogram of training images. RGB Color Histogram attributes are used to train the KNN classification algorithm. Trained K-Nearest Neighbor (KNN) is classified to scan live webcam scene by scene to perform extraction of features from each frame, and then the color is identified by trained K-Nearest Neighbor (KNN) classifier. Color identification is an area of research and interest for more than five decades. Some of the research surveys are given below. Manuel G. et.al., 2020 used the method of the extreme color channel, compared with the other two within RGB. Observations: The target color channel value is compared to the other two-color channels in the equations. The color with the greatest channel value is the target color. Jayme et.al., 2016 used the method of three-color models RGB, HSV, and CIE Lab to determine the final result. Observations: Color transformation is applied using RGB-related equations. The best result was selected after applying transformation 3 times. I.Al-Bahadlya et.al., 2005 used the method of the intensity of the color determined using two-color models RGB and CMY. Observations: Color pixels are taken as inputs and recorded as RGB then converted to CMY intensities. Trupti et.al., 2013 used the method of Image Segmentation Using XYZ Color Plane. Observations: RGB values are converted into XYZ color space value Then thresholding is applied to anyone's plane. Shamik et.al., 2002 used the method of Image Segmentation Using Features from the HSV Color Space. Observations: The RGB value of a pixel is first