Section outline

  • Project: Ball-Tracking Turret Using OpenCV

    In this section, we will apply all the concepts learned so far in a practical and engaging project—a camera-controlled turret that tracks and follows a moving colored ball. This project integrates hardware and software: image processing with OpenCV and physical actuation using servo motors controlled by Arduino or Raspberry Pi. It mimics how real-world vision-based robots perform tracking tasks like surveillance, sorting, or interaction.

    • 1. Project Overview and Objective
      The goal is to build a simple robotic turret that can rotate and tilt to follow a specific color object, such as a red or blue ball. The camera identifies the position of the ball in its frame, and based on the ball’s location, it adjusts the servos to center the ball on screen.

      This project involves:

      • Camera input via USB or Pi Camera
      • OpenCV for object detection
      • Arduino or Raspberry Pi for servo control
      • Servo mount for horizontal and vertical motion (2 DOF)

      2. Setting Up the Hardware

      • Camera: Mount it securely to see the tracking area.
      • Servo motors: Connect two micro servos for pan and tilt movement.
      • Microcontroller: Use Arduino Uno or connect Raspberry Pi GPIOs if you're using Python to control servos directly.
      • Power supply: Ensure servos are powered externally to avoid current issues.

      Servo connection tips:

      • Use PWM-capable pins
      • Use Servo.h library on Arduino
      • Connect camera to Raspberry Pi if using Python with OpenCV

      3. Ball Detection Using OpenCV

      To detect a colored ball, use HSV color space for robust detection under different lighting:

      hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv, lower_color, upper_color)
contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

      Once you find the largest contour, calculate its center and use that to adjust servo angles accordingly.

      4. Servo Movement Logic

      Compare the center of the ball with the center of the frame. If the ball is left of center, rotate servo to the left; if above center, tilt the camera up, and so on. This real-time feedback loop allows smooth tracking.

      Example pseudocode:

      if ball_x < center_x:
    pan_angle -= 1
elif ball_x > center_x:
    pan_angle += 1

if ball_y < center_y:
    tilt_angle -= 1
elif ball_y > center_y:
    tilt_angle += 1

      5. Testing and Calibration

      • Test detection using different colors and distances
      • Calibrate servo angles to prevent overshooting
      • Add range limits to prevent servo from rotating too far

      6. Extensions and Variations

      • Track multiple objects and switch targets
      • Use voice commands to activate tracking
      • Integrate object size estimation to control zoom

      This project combines many core robotic concepts—mechanical control, image processing, real-time feedback, and calibration. It is an exciting step toward building robots that can truly sense and respond to the environment dynamically.