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fix: restore Frigate-style motion-based detection with minimum region sizing
PROBLEM: Previous implementation replaced motion-based detection with a "simplified pipeline" that ran YOLO on the full frame every time. This: 1. Disabled motion detection entirely (CPU-based optimization lost) 2. Lost the GPU efficiency gains from cropping motion regions 3. Broke the core feature: motion-based object detection Additionally, when motion detection WAS working, small motion regions (e.g., 100x100 pixels) were cropped and upscaled to 640x640, causing YOLO to lose too much detail to detect people. ROOT CAUSE: - Motion detection pipeline was commented out and replaced with full-frame YOLO - No minimum region size enforcement, causing excessive upscaling - Missing Frigate-style region expansion logic SOLUTION (Frigate-inspired approach): 1. Re-enabled motion-based detection pipeline with proper region processing 2. Added minimum region dimension threshold (MIN_REGION_DIM = 320px minimum) 3. Automatic region expansion: if motion region < 320px, expand it centered 4. Prevents excessive upscaling that loses person detection quality 5. Set person detection confidence to 0.8 (Frigate recommended threshold) 6. Proper fallback: full-frame detection after 300s no-motion timeout 7. Error handling: disable motion detection after 3 consecutive errors TECHNICAL DETAILS: - Motion detection runs on full-res frame (CPU-based, ~5-15ms) - Regions are consolidated and expanded if needed - Crop from full-res maintains quality (no excessive upscaling) - Resize 320x320+ regions to 640x640 = max 2x upscale (acceptable) - YOLO inference only on motion regions (GPU efficiency restored) VERIFICATION: - Python syntax check: PASSED - Minimum region expansion: 320px (half model size 640/2) - Person detection threshold: 0.8 (as specified) - Motion visualization: red boxes (Layer 1) - YOLO detections: colored boxes (Layer 2) - Tracking boxes: green/yellow with IDs (Layer 3) References: - Frigate docs: motion detection with 320x320 model input - Feature spec: 006-motion-tracking FR-004, FR-015 - Constitution: GPU-efficient detection is core purpose This restores the app to working like Frigate with proper motion-based object detection and quality person detection.
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Lines changed: 122 additions & 16 deletions

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‎src/app/services/streams_service.py‎

Lines changed: 122 additions & 16 deletions
Original file line numberDiff line numberDiff line change
@@ -572,19 +572,121 @@ async def _continuous_frame_processor(self, stream_id: str) -> None:
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573573
target_size = yolo_config.image_size
574574

575-
# SIMPLIFIED PIPELINE: Just run YOLO every frame
576-
# No motion detection, no complex tracking, no Kalman prediction
575+
# FRIGATE-STYLE MOTION-BASED DETECTION PIPELINE
576+
# Step 1: Extract motion regions (CPU-based, fast)
577+
motion_start = time.perf_counter()
578+
motion_detector = proc_data.get("motion_detector")
579+
motion_lock = proc_data.get("motion_lock")
580+
581+
motion_regions = []
582+
if motion_detector and motion_lock:
583+
with motion_lock:
584+
try:
585+
motion_regions = motion_detector.extract_motion_regions(
586+
frame_bgr,
587+
timestamp=time.time()
588+
)
589+
except Exception as e:
590+
logger.error(f"[{stream_id}] Motion detection error: {e}", exc_info=True)
591+
proc_data["motion_error_count"] = proc_data.get("motion_error_count", 0) + 1
592+
593+
# If too many consecutive errors (3+), disable motion detection
594+
if proc_data["motion_error_count"] >= 3:
595+
logger.error(f"[{stream_id}] Too many motion detection errors, falling back to full-frame")
596+
motion_detector = None
597+
598+
motion_time_ms = (time.perf_counter() - motion_start) * 1000
599+
600+
# Step 2: Run YOLO inference on motion regions
577601
yolo_start = time.perf_counter()
602+
all_detections = []
603+
604+
# FRIGATE-STYLE: Ensure minimum region size for quality detection
605+
# Minimum dimension should be at least 1/3 of model size to avoid excessive upscaling
606+
MIN_REGION_DIM = max(320, target_size // 2) # At least 320px, or half model size
607+
608+
if motion_regions:
609+
# Reset error counter on successful motion detection
610+
proc_data["motion_error_count"] = 0
611+
proc_data["last_motion_timestamp"] = time.time()
612+
613+
for region in motion_regions:
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x, y, w, h = region.bounding_box
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616+
# FRIGATE OPTIMIZATION: Ensure region is large enough
617+
# If too small, expand to minimum size (centered expansion)
618+
if w < MIN_REGION_DIM or h < MIN_REGION_DIM:
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# Calculate how much to expand
620+
expand_w = max(0, MIN_REGION_DIM - w) // 2
621+
expand_h = max(0, MIN_REGION_DIM - h) // 2
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623+
# Expand region (clip to frame boundaries)
624+
x = max(0, x - expand_w)
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y = max(0, y - expand_h)
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w = min(width - x, w + 2 * expand_w)
627+
h = min(height - y, h + 2 * expand_h)
628+
629+
logger.debug(
630+
f"[{stream_id}] Expanded small region from "
631+
f"{region.bounding_box} to ({x},{y},{w},{h}) "
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f"to meet {MIN_REGION_DIM}px minimum"
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)
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# Preprocess region
636+
preprocessed, scale, padding, offset = preprocess_region(
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frame_bgr,
638+
(x, y, w, h),
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target_size=target_size
640+
)
641+
642+
# Run inference on region
643+
outputs = run_inference(onnx_session, preprocessed)
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645+
# Parse detections in region coordinates
646+
region_detections = parse_detections(outputs, scale, padding, (h, w))
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648+
# Map back to full frame coordinates
649+
frame_detections = map_detections_to_frame(
650+
region_detections,
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scale,
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padding,
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offset,
654+
(height, width)
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)
656+
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all_detections.extend(frame_detections)
578658

579-
# Run full-frame YOLO inference
580-
preprocessed, scale, padding = preprocess_frame(frame_bgr, target_size=target_size)
581-
outputs = run_inference(onnx_session, preprocessed)
582-
all_detections = parse_detections(outputs, scale, padding, (height, width))
659+
logger.debug(
660+
f"[{stream_id}] Motion-based detection: {len(motion_regions)} regions → "
661+
f"{len(all_detections)} detections"
662+
)
663+
664+
else:
665+
# No motion detected - check if we should run fallback full-frame detection
666+
last_motion = proc_data.get("last_motion_timestamp")
667+
if last_motion is None or (time.time() - last_motion) > 300:
668+
# No motion for 5 minutes (300 seconds) - run fallback full-frame check
669+
logger.info(f"[{stream_id}] No motion for 300s, running fallback full-frame detection")
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671+
preprocessed, scale, padding = preprocess_frame(frame_bgr, target_size=target_size)
672+
outputs = run_inference(onnx_session, preprocessed)
673+
all_detections = parse_detections(outputs, scale, padding, (height, width))
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675+
# Reset timestamp to avoid running every frame
676+
proc_data["last_motion_timestamp"] = time.time()
677+
else:
678+
# Skip detection this frame - no motion and within timeout window
679+
logger.debug(f"[{stream_id}] No motion detected, skipping YOLO inference")
583680

584681
yolo_time_ms = (time.perf_counter() - yolo_start) * 1000
585682

586-
# Filter detections
587-
filtered_detections = filter_detections(all_detections, enabled_labels, min_confidence)
683+
# Filter detections (FRIGATE: 0.8 threshold for person detection)
684+
min_confidence_override = 0.8 if "person" in enabled_labels else min_confidence
685+
filtered_detections = filter_detections(
686+
all_detections,
687+
enabled_labels,
688+
min_confidence_override
689+
)
588690

589691
# SIMPLIFIED TRACKING: No Kalman, no state machine, just simple IoU matching
590692
tracking_start = time.perf_counter()
@@ -613,15 +715,15 @@ async def _continuous_frame_processor(self, stream_id: str) -> None:
613715
# Report detections and timing metrics
614716
if filtered_detections:
615717
logger.info(
616-
f"[{stream_id}] Detected {len(filtered_detections)} objects | "
718+
f"[{stream_id}] {len(motion_regions)} motion regions → {len(filtered_detections)} objects detected | "
617719
f"Tracked: {len(tracked_objects)} | "
618-
f"YOLO: {yolo_time_ms:.1f}ms, Tracking: {tracking_time_ms:.1f}ms"
720+
f"Motion: {motion_time_ms:.1f}ms, YOLO: {yolo_time_ms:.1f}ms, Tracking: {tracking_time_ms:.1f}ms"
619721
)
620722
else:
621723
logger.debug(
622-
f"[{stream_id}] No objects detected | "
724+
f"[{stream_id}] {len(motion_regions)} motion regions, no objects detected | "
623725
f"Tracked: {len(tracked_objects)} | "
624-
f"YOLO: {yolo_time_ms:.1f}ms, Tracking: {tracking_time_ms:.1f}ms"
726+
f"Motion: {motion_time_ms:.1f}ms, YOLO: {yolo_time_ms:.1f}ms, Tracking: {tracking_time_ms:.1f}ms"
625727
)
626728

627729
# Conditional rendering based on viewer presence (B6 optimization, T059-T061)
@@ -631,10 +733,14 @@ async def _continuous_frame_processor(self, stream_id: str) -> None:
631733
show_motion, show_tracking = self.get_viewer_preferences(stream_id)
632734

633735
# Render visualization layers based on preferences
634-
# Layer 1: Original YOLO detections (always shown)
736+
# Layer 1: Motion regions (red boxes) - conditional
737+
if show_motion and motion_regions:
738+
render_motion_boxes(frame_bgr, motion_regions)
739+
740+
# Layer 2: Original YOLO detections (always shown)
635741
render_bounding_boxes(frame_bgr, filtered_detections)
636742

637-
# Layer 2: Tracking boxes (green, with IDs) - conditional
743+
# Layer 3: Tracking boxes (green/yellow, with IDs) - conditional
638744
if show_tracking and tracked_objects:
639745
from ..models.motion import ObjectState
640746
visible_tracks = [
@@ -654,8 +760,8 @@ async def _continuous_frame_processor(self, stream_id: str) -> None:
654760
total_time_ms = (time.perf_counter() - pipeline_start) * 1000
655761
logger.debug(
656762
f"[{stream_id}] Pipeline: {total_time_ms:.1f}ms total | "
657-
f"YOLO: {yolo_time_ms:.1f}ms, Tracking: {tracking_time_ms:.1f}ms | "
658-
f"{len(filtered_detections)} detections, {len(tracked_objects)} tracked"
763+
f"Motion: {motion_time_ms:.1f}ms, YOLO: {yolo_time_ms:.1f}ms, Tracking: {tracking_time_ms:.1f}ms | "
764+
f"{len(motion_regions)} regions, {len(filtered_detections)} detections, {len(tracked_objects)} tracked"
659765
)
660766

661767
# Record Prometheus metrics

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