The engine was searching an empty room fifteen times a second

Measured rather than guessed, and the first guess was wrong. Wall clock
said H.265 decode cost 58 ms a frame; cap.read() blocks until the next
frame arrives, so that was the frame interval, not work. As CPU time:
decode 3.7 ms, detection 31.0 ms - and detection ran on every frame
whether or not anything was in front of the camera, 6,649 of 8,634
frames with faces_seen 0 and active_tracks 0 throughout.

detect_threads: OpenCV spreads a small repeated job over eight threads,
costing 31.0 ms of CPU for 8.9 ms of wall. One thread costs 15.3 ms for
15.3 ms, against a 66 ms budget at 15 fps. Half the CPU for latency
nothing can notice.

motion_gate: a 160x90 greyscale absdiff, 0.1 ms against detection's 15.
Consulted only while no track is open; forced to look every
motion_max_skip frames; compared against the last frame SEARCHED so a
slow drift cannot creep under the threshold; and a threshold above this
camera's measured noise and far below a person, so anything ambiguous
detects. tests/test_motion_gate.py pins each of those rather than the
saving, including asserting the longest run of skips rather than the
total - counting the total would pass a gate that slept forty frames
and then looked forty times.

Together 80% -> 16% of a core, detection skipped on 92% of frames.
faces_seen is still 0 and the gate is not why: run directly over the
same frames the detector finds nothing at threshold 0.50 either. The
placement is the limit, as recorded; the CPU was being spent to
rediscover that fifteen times a second.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01KGcjxF1cNLcuwc3DAPcnfj

behavision/config.py

@@ -132,6 +132,29 @@ class AppSection(BaseModel):
     # on changes what the system is under GDPR and India's DPDP, so it has to
     # be a decision somebody makes rather than one they inherit.
     store_faces: bool = False
+    # How many threads OpenCV may use for detection. Measured on the office
+    # camera (800x448 sub-stream): the default of 8 costs 31 ms of CPU per
+    # frame for 8.9 ms of wall time, while ONE thread costs 15.3 ms of CPU for
+    # 15.3 ms of wall - half the CPU for 6 ms more latency, against a 66 ms
+    # frame budget at 15 fps. The default is wrong here because OpenCV sizes it
+    # for one big job on an idle machine, and this is a small job repeated
+    # forever on a machine also running the recogniser, the tracker and three
+    # other cameras. 0 leaves OpenCV's own default alone.
+    detect_threads: int = 1
+    # Skip detection on frames where nothing has changed and nothing is being
+    # tracked. A shop is empty most of the day and a frame of an empty room
+    # costs exactly as much to search as a busy one. See CameraWorker.run for
+    # why this cannot lose a face.
+    motion_gate: bool = True
+    # Mean absolute difference, 0-255, over a 160x90 greyscale thumbnail. 1.0
+    # is well below the noise floor of a real camera - measured on this one,
+    # an empty room varies by ~0.3 between frames - so it triggers on movement
+    # rather than on sensor noise, and anything ambiguous detects.
+    motion_threshold: float = 1.0
+    # Detect at least this often regardless of the gate, so a change the
+    # thumbnail cannot see - someone entering at the far edge, a slow lean into
+    # frame - is still found within a second.
+    motion_max_skip: int = 12
 
 
 class ApiSection(BaseModel):