Motion detect fraction small area of frame

counter_live_rknn.py

@@ -111,6 +111,14 @@ IS_LIVE = SOURCE.lower().startswith(("rtsp://", "http://"))
 
 MOTION_DETECTION_ENABLED = os.getenv("MOTION_DETECTION_ENABLED", "false").lower() == "true"
 MOTION_THRESHOLD = float(os.getenv("MOTION_THRESHOLD", "5.0"))
+# Per-pixel intensity change (0-255) for a pixel to count as "moved".
+MOTION_PIXEL_DELTA = int(os.getenv("MOTION_PIXEL_DELTA", "25"))
+# Fraction of frame pixels that must change (0-1) to trigger inference. Small,
+# so an object entering the edge of the frame is detected immediately.
+MOTION_MIN_AREA_FRAC = float(os.getenv("MOTION_MIN_AREA_FRAC", "0.002"))
+# Always run inference at least every N frames even if no motion (heartbeat), so a
+# slow/stationary object is never missed for long.
+MOTION_HEARTBEAT_FRAMES = int(os.getenv("MOTION_HEARTBEAT_FRAMES", "15"))
 
 CROSS_FLASH_FRAMES = 12
 POPUP_LIFETIME = 20
@@ -1078,6 +1086,7 @@ def run():
     frame_idx = 0
     inf_ms = 0.0
     prev_gray = None
+    frames_since_infer = 0
     video_writer = None
     crossing_times = deque()
     counter_in = 0
@@ -1138,15 +1147,21 @@ def run():
             gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
             if prev_gray is not None:
                 diff = cv2.absdiff(gray, prev_gray)
-                mean_diff = cv2.mean(diff)[0]
-                skip_inference = mean_diff < MOTION_THRESHOLD
+                moved = int(np.count_nonzero(diff > MOTION_PIXEL_DELTA))
+                moved_frac = moved / diff.size
+                skip_inference = moved_frac < MOTION_MIN_AREA_FRAC
+                if frames_since_infer >= MOTION_HEARTBEAT_FRAMES:
+                    skip_inference = False
             prev_gray = gray
 
         detections = []
         if not skip_inference:
+            frames_since_infer = 0
             inf_start = time.time()
             detections = model(frame)
             inf_ms = inf_ms * 0.9 + (time.time() - inf_start) * 1000 * 0.1
+        else:
+            frames_since_infer += 1
 
         object_boxes_xyxy = []
         object_scores = []