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.
As edge AI matures, you will find more URL endpoints like: http://camera/api/v2/multicamera?mode=tensorflow&track_id=person_001 inurl multicameraframe mode motion work
import cv2 import numpy as np cap = cv2.VideoCapture('mosaic_stream.mp4') ret, frame = cap.read() h, w = frame.shape[:2] cell_w, cell_h = w // 2, h // 2 Define quadrants: top-left, top-right, bottom-left, bottom-right quadrants = [ (0,0,cell_w,cell_h), (cell_w,0,w,cell_h), (0,cell_h,cell_w,h), (cell_w,cell_h,w,h) ] Motion mode activation prev_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) Set up a test bench with two cheap
"frame_id": "2024-05-20T14:32:00Z", "layout": "2x2", "motion_events": [ "camera": 2, "confidence": 87, "bbox": [120, 80, 300, 420] , "camera": 4, "confidence": 45, "bbox": [640, 200, 800, 600] ] As edge AI matures, you will find more