This 2-hour course introduces learners to combining Arduino with computer vision using Python and OpenCV. Starting with a setup of the Arduino board and Python environment, the course guides students on installing OpenCV, connecting cameras, and capturing video streams for real-time processing.

Students learn essential computer vision techniques such as color detection, object tracking, edge detection, and contour recognition. The course demonstrates how to interpret visual data and translate it into actionable signals for Arduino-controlled hardware. This includes controlling motors, LEDs, and other actuators based on detected objects or motion.

Through hands-on examples, learners develop skills in integrating software and hardware, creating interactive projects like smart surveillance, automated robotics, and gesture-controlled devices. By the end of the course, participants will be able to design systems that respond dynamically to visual inputs, bridging the gap between Arduino electronics and computer vision technology. This course is ideal for hobbyists, robotics enthusiasts, and anyone interested in practical applications of computer vision with microcontrollers.