\section{Conclusion}
\label{sec:conclusion}

We introduced CHURI, a detector-gated tracking architecture for resource-constrained retail environments that achieves 78.2\% recall on overhead fisheye surveillance through zone-level background subtraction with occupancy-aware dual learning rates. By storing background statistics at zone level ($16\times16$ grid, 6.1 KB per camera) rather than per-pixel (37.4 MB), CHURI enables 8-camera deployment on single Raspberry Pi 5 (8GB RAM) versus 2-4 cameras for baseline methods---a $6{,}131\times$ memory reduction that validates resource-first design for affordable edge deployment.

Three contributions enable this result. First, zone-level spatial discretization (256 zones vs 1.5M pixels) captures person-scale motion while reducing background model memory by $6{,}131\times$, leaving 81.9\% RAM headroom for system operations on commodity edge devices. Second, occupancy-aware dual learning rates (LR\_slow=0.0003 for person zones, LR\_fast=0.5 for background) prevent stationary absorption that causes classical BGS to degrade to 38-42\% recall, while sticky tracking sustains trajectories for 50+ frames across detector dropouts. Third, gated YOLO verification at 8\% invocation rate reduces detector budget by $12\times$ while maintaining tracking continuity, achieving 78.2\% recall on WEPDTOF (+26.1pp over YOLO), 87.6\% on MERL Shopping (+52.6pp), and 92.1\% on Innovatiana behavioral anomalies (+7.6pp).

CHURI demonstrates that multi-camera edge tracking requires inverting the traditional accuracy-first paradigm: rather than optimizing detector performance and then attempting compression, we design the minimal background model sufficient for tracking continuity when fused with sparse detector confirmations. This resource-constrained approach achieves comparable accuracy to per-pixel methods while enabling $4\times$ more cameras per device, validating zone-level background subtraction as a practical solution for small retail deployment at 1/70th traditional infrastructure cost (\$80 Raspberry Pi 5 vs \$8,000 GPU workstations).
