Object Tracker Based on Color

Object Tracker Based on Color

Abstract:

“Object Tracker Based on Color” project summary. This project aims to create a real-time RGB-color tracking system. Subtracting RGB color components, eliminating noise, and converting pictures to binary improves tracking. The project detects and displays RGB objects using blob analysis and bounding boxes. The system can track colorful objects and live video, but it is less accurate in low-resolution camera footage and poor lighting.

Introduction:

Computer vision, surveillance, robotics, and image processing all use object tracking. Conventional object tracking uses motion patterns or characteristics. This project tracks items by RGB color. A color-based object tracking system is our goal.

Objective:

This project aims to construct a real-time RGB-based object tracking system. The system uses powerful preprocessing and analytic methods to track efficiently and reliably. The project shows color-based object tracking’s pros and cons.

Project Details:

To track objects accurately, the suggested system takes steps. Each video frame is preprocessed by eliminating RGB color components from the grayscale image and filtering noise. This detects just required colors. For analysis, the grayscale image is transformed to binary.

Blob analysis is used to detect RGB items. This method identifies and segments items by color. Finally, bounding box methodology draws rectangular rectangles around tracked items to indicate their presence.

Conclusion:

This project’s RGB-based object tracking system shows promise for accurate tracking. Preprocessing, blob analysis, and bounding box methods enable real-time tracking of colored objects in live video.

The system has limitations, though. Low-resolution camera footage or inadequate lighting may reduce its accuracy. Several factors can impair object detection and tracking. Nonetheless, the system can track multiple colored objects, enabling applications in numerous sectors.