To reconstruct the background of a video that is behind moving foreground objects, you can use a technique called background subtraction. This technique involves subtracting the current frame of the video from a background model that has been previously constructed. The resulting difference image will highlight the areas where there is motion in the scene.

Here is an example code using OpenCV library in Python to perform background subtraction:

```python
import cv2

# Create a background subtractor object
bg_subtractor = cv2.createBackgroundSubtractorMOG2()

# Open the video file
cap = cv2.VideoCapture('video.mp4')

# Loop through the frames of the video
while True:
# Read the next frame
ret, frame = cap.read()

# Apply background subtraction
fg_mask = bg_subtractor.apply(frame)

# Display the resulting foreground mask
cv2.imshow('Foreground Mask', fg_mask)

# Exit if the user presses 'q'
if cv2.waitKey(1) & 0xFF == ord('q'):
break

# Release the video capture object and close all windows
cap.release()
cv2.destroyAllWindows()
```

In this code, we first create a background subtractor object using the `cv2.createBackgroundSubtractorMOG2()` function. We then open the video file using the `cv2.VideoCapture()` function and loop through the frames of the video. For each frame, we apply background subtraction using the `bg_subtractor.apply()` function and display the resulting foreground mask using the `cv2.imshow()` function. Finally, we exit the loop if the user presses the 'q' key and release the video capture object and close all windows using the `cap.release()` and `cv2.destroyAllWindows()` functions, respectively.

Note that this code will only display the foreground mask, which highlights the areas where there is motion in the scene. To reconstruct the background of the video, you will need to use the foreground mask to mask out the moving foreground objects and then use the remaining pixels to reconstruct the background. This can be done using techniques such as inpainting or background replacement.
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