Image-Based Background Subtraction Through the Application of Running Average Technique
The Running Average method is a computer vision technique used for separating moving foreground from static background. This method is particularly useful for object tracking, motion detection, and surveillance systems.
How Does It Work?
The Running Average method maintains and continuously updates a background model using a weighted average of pixel values from consecutive video frames. Essentially, it creates a smooth, gradually-updated estimate of the static background, allowing moving objects to be detected as deviations from this background.
The mathematical formula for the Running Average method involves the current frame, the background model, and a learning rate. The learning rate, controlling how fast the background adapts, is the parameter . A smaller results in slower adaptation (good for mostly static scenes), while a larger allows faster background changes to be incorporated (useful if background shifts occur).
In practice, the initial background is often set as the first frame or an average of the first few frames. As each new frame arrives, the running average updates the background model. Foreground objects are detected by subtracting the background model from the current frame and thresholding the difference to isolate moving regions.
Implementation
Before implementing the Running Average method, ensure you have Python 3.x, NumPy, OpenCV, and a working webcam or camera module. The function used for the Running Average method in OpenCV is .
Here's a summary of the overall logic of the Running Average Method:
- Capture video frames.
- Initialize the background model.
- Update the background model for each frame.
- Convert the floating-point background back to an image format.
- Display both the current frame and the updated background.
- Stop when the user presses the Esc key.
In the example code, the program captures live video from a webcam and uses the Running Average method to create a smooth background model over time.
Case Study
In Case 2, if a hand is waved quickly, the background model starts showing the hand as transparent, since it blends slowly and still emphasizes the static background. This gradual adaptation helps suppress temporary disturbances, such as passing birds, because the background adapts gradually rather than instantly.
This method is straightforward, computationally efficient, and effective for object tracking and motion detection where the background is largely constant or slowly varying.
[1] [Source 1] [2] [Source 2] [3] [Source 3]
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