feat: initial BantayCam scaffold

- FastAPI backend with async SQLAlchemy
- Camera RTSP management (add, start, stop)
- Vehicle detection (YOLO + fast-alpr)
  - Type: car, motorcycle, truck, jeepney
  - Color detection (HSV)
  - License plate OCR
  - Motorcycle person count
- Face detection + InsightFace ArcFace embedding
- pgvector identity grouping (auto-cluster same face)
- Vehicle + person movement trail APIs
- Docker Compose with pgvector/pg16
- Models: Camera, VehicleIdentity, VehicleEvent, PersonIdentity, FaceEvent

backend/app/models/person.py

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+from sqlalchemy import Column, String, Integer, Float, DateTime, Text, ForeignKey, JSON
+from sqlalchemy.dialects.postgresql import UUID, ARRAY
+from sqlalchemy.sql import func
+from pgvector.sqlalchemy import Vector
+import uuid
+
+from app.core.database import Base
+
+
+class PersonIdentity(Base):
+    """
+    Unique person identity — grouped by face embedding similarity.
+    All face events linked to this identity let us trace where this person has been.
+    """
+    __tablename__ = "person_identities"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    name = Column(String(255), nullable=True)                # e.g. "Juan Dela Cruz" if registered
+    label = Column(String(100), nullable=True)               # e.g. "Unit 3A Resident", "Delivery Rider"
+    notes = Column(Text, nullable=True)
+    is_registered = Column(Integer, default=0)               # 1 = manually registered with name
+    is_watchlisted = Column(Integer, default=0)              # 1 = flagged for alerts
+    thumbnail_path = Column(Text, nullable=True)             # Best face shot
+
+    # Face embedding (512-dim for InsightFace buffalo_l)
+    embedding = Column(Vector(512), nullable=True)
+
+    # Stats
+    first_seen_at = Column(DateTime(timezone=True), nullable=True)
+    last_seen_at = Column(DateTime(timezone=True), nullable=True)
+    total_sightings = Column(Integer, default=0)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+
+    def __repr__(self):
+        return f"<PersonIdentity {self.name or 'Unknown'} ({self.total_sightings} sightings)>"
+
+
+class FaceEvent(Base):
+    """
+    Every face detection event — one row per detected face per frame.
+    """
+    __tablename__ = "face_events"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    camera_id = Column(UUID(as_uuid=True), ForeignKey("cameras.id"), nullable=False, index=True)
+    identity_id = Column(UUID(as_uuid=True), ForeignKey("person_identities.id"), nullable=True, index=True)
+
+    # Detection
+    detection_confidence = Column(Float, nullable=True)
+    face_bbox = Column(JSON, nullable=True)                  # {"x","y","w","h"}
+    face_embedding = Column(Vector(512), nullable=True)      # Per-event embedding for re-clustering
+    similarity_score = Column(Float, nullable=True)          # Match score to identity
+
+    # Context — if detected alongside a vehicle event
+    vehicle_event_id = Column(UUID(as_uuid=True), ForeignKey("vehicle_events.id"), nullable=True)
+
+    # Storage
+    snapshot_path = Column(Text, nullable=True)             # Full frame
+    face_snapshot_path = Column(Text, nullable=True)        # Cropped face chip
+
+    # Meta
+    captured_at = Column(DateTime(timezone=True), server_default=func.now(), index=True)
+
+    def __repr__(self):
+        return f"<FaceEvent identity={self.identity_id} cam={self.camera_id} at={self.captured_at}>"