\section{Discussion}
\label{sec:discussion}

\textbf{When Zone-BGS Excels.}
CHURI achieves strongest gains in scenarios where YOLO struggles but motion persists: sparse density (empty\_store 100\% vs YOLO 25\%), stationary shoppers (call\_center +37.2\% through occupancy-aware learning), and prolonged occlusions (it\_office +73.2\% via sticky tracking). The zone-level approach proves particularly effective on overhead fisheye where COCO-pretrained detectors degrade to 52.1\% recall---coarse spatial resolution ($90\times67.5$ pixel zones) remains sufficient for person-scale motion detection when fused with YOLO verification. Multi-camera scalability represents the primary advantage: $6{,}131\times$ memory reduction enables 8-camera deployment on single Raspberry Pi 5 versus 2-4 cameras for baseline methods, validating resource-first design for edge deployment.

\textbf{Limitations and Failure Modes.}
Zone-level discretization causes false positives in scenarios with reflective surfaces (jewelry\_store 102.4\% recall) or oscillating backgrounds (ceiling fans, monitors). While oscillation-aware suppression reduces this by 35\%, it cannot eliminate all environmental artifacts without risking genuine motion suppression. Boundary effects occur when people straddle zone edges---only zones with sufficient foreground ratio trigger, causing partial detections. Small objects ($<$$50\times50$ pixels) may be missed if contained within a single zone, though sticky tracking mitigates this through temporal persistence. Cold-start performance shows higher false positive rate during initial 30 frames as zone statistics initialize.

\textbf{Deployment Considerations.}
CHURI targets small retail (800-1200 sq ft, 4-8 cameras) where \$200-500 budgets preclude GPU infrastructure. Larger stores ($>$2000 sq ft) may justify dedicated NVR workstations with per-pixel BGS, while cloud-based systems suit enterprises with IT support. The $16\times16$ grid proves optimal for $1440\times1080$ fisheye cameras---higher resolutions may benefit from $20\times20$ or $24\times24$ grids to maintain 80-100 pixel zone size. Zone dimensions below $50\times50$ pixels risk noise sensitivity, while above $150\times150$ pixels lose spatial resolution for tracking. The 2 FPS processing rate balances real-time responsiveness (alerts within 500ms) with CPU headroom for 8-camera deployment on quad-core ARM processors.
