This project showcases the development of an autonomous car capable of detecting and following a path using computer vision techniques. The car processes real-time video input to identify contours and adjust its motion dynamically.

Key Features:

• Path Detection: Captures a live video feed and identifies the path using edge detection and contour extraction. The largest contour is analyzed to determine the centroid, guiding the car's direction.
• Dynamic Motor Control: Adjusts the car's motion based on the centroid's position relative to the frame, enabling forward movement, left and right turns, or stopping.
• Computer Vision Processing: Utilizes OpenCV for video processing, including grayscale conversion, Gaussian blurring, and Canny edge detection, ensuring efficient detection and reduced noise.
• Obstacle Handling: Adjusts car behavior to handle significant deviations or ambiguous centroid positions, avoiding errors or obstacles.

This project highlights the integration of real-time image processing, embedded system control, and robotics, paving the way for autonomous navigation systems.

Technologies Used: Python, OpenCV, Rosmaster Library, Embedded Systems