Set up a test bench with two cheap USB webcams, apply the Python script above, and experiment with the threshold values. Once you see “MOTION detected in Camera 1” appear in your console within 100ms, you’ll have successfully reverse-engineered the core logic behind thousands of commercial VMS products. Keywords integrated for semantic SEO: inurl scanner, multi-camera motion detection, frame-based analytics, video motion mode, surveillance software architecture.
for idx, (x1,y1,x2,y2) in enumerate(quadrants): cell_prev = prev_gray[y1:y2, x1:x2] cell_curr = gray[y1:y2, x1:x2] diff = cv2.absdiff(cell_prev, cell_curr) motion = np.sum(diff > 25) # Threshold of 25 if motion > (cell_w * cell_h * 0.01): # 1% of pixels changed print(f"MOTION detected in Camera idx+1") cv2.rectangle(frame, (x1,y1), (x2,y2), (0,0,255), 3) inurl multicameraframe mode motion work
ffmpeg -i rtsp://cam1/stream -i rtsp://cam2/stream \ -i rtsp://cam3/stream -i rtsp://cam4/stream \ -filter_complex "xstack=inputs=4:layout=0_0|w0_0|0_h0|w0_h0" \ -f image2 pipe:1 Write a Python script that reads the mosaic frame and applies motion detection per quadrant. Set up a test bench with two cheap