OBJECT TRACKING USING OPEN CV PYTHON

To implement Object Tracking using OpenCV in Python, here's a detailed step-by-step guide to setting it up, running it, and understanding how its works :

1. Introduction to Object Tracking:

Object tracking is an essential task in computer vision, used in various applications like robotics, sports analysis, and video surveillance. OpenCV, a powerful library for computer vision, provides various methods for object tracking, such as:

• Color-based tracking
• Template matching
• Advanced techniques like MeanShift, CamShift, etc.

This system detects and tracks an object from a video stream or an image by drawing a bounding box around it.

2. Language and Interface:

• Language: Python, a versatile and widely used programming language.
• Interface: Visual Studio Code (VS Code) is recommended for running the code.

3. Required Modules or Packages:

• cv2 (OpenCV): This library is essential for image and video processing tasks. It provides tools for reading, writing, and manipulating images and videos.

To install OpenCV, use the following command in your terminal or command prompt:

4. How to Run the Code:
Step 1: Download and install Visual Studio Code from the VS Code official website.
Step 2: Open the Command Prompt (CMD) as Administrator and install OpenCV using pip.
Step 3: Open Visual Studio Code.
Step 4: Create a file named objecttracking.py.
Step 5: Copy and paste the code below into the file.
Step 6: Save the file.
Step 7: Run the code by clicking the "Run" button in VS Code.
5. Python Code for Object Tracking Using OpenCV: 

  
 
 
 
 
 
 
 
 
 
 
 
 
 6. Code Explanation:
cv2.VideoCapture(0): Captures video from the webcam (0 represents the default camera).
TrackerMOSSE_create(): Creates a MOSSE tracker, one of the fastest tracking algorithms in OpenCV.
cv2.selectROI: Allows you to select a region of interest (ROI) for tracking.
drawBox: This function draws a rectangle around the tracked object using its bounding box coordinates.
tracker.update: Updates the tracker with the current frame and returns the new bounding box.
cv2.imshow: Displays the frame with the tracking box.
7. FPS Calculation (Optional):
If you want to calculate and display the Frames Per Second (FPS), add this to the loop: 
 
 
 
8. Running the Code:
Once you run the code in VS Code, you will be prompted to select an object to track by drawing a rectangle around it. The tracker will then follow the object's movement in real-time.
9. Use Cases:
Robotics: Track objects for manipulation or navigation.
Sports Analysis: Track players or balls during games.
Video Surveillance: Track moving objects in surveillance footage.