\begin{abstract}

Most retail tracking solutions assume always-on cloud analytics, making basic counting a subscription-based service. For small stores (47.3\% of establishments), \$40{-}120/month per camera can exceed 2{-}6\% profit margins, so tracking is disabled and CCTV stays passive. Yet retail is harder than benchmarks: overhead fisheye views need specialized line-crossing or top-down models, and accurate tracking often requires GPU-class inference (cloud or \$8{,}000+ NVRs)-well beyond \$200{-}500 small-store budgets despite occupancy needs for compliance and theft prevention. At this operating point, existing edge methods fail: standalone YOLO on Raspberry Pi 4B achieves 52.1\% recall while saturating CPU at 82\% for single-camera processing; classical BGS drifts without detector confirmation (MOG2: 40.5\%, KNN: 54.8\%); and sparse detection (YOLO every $N$ frames) causes 35{-}48\% ID-switch rates due to fragmented trajectories. We introduce \textbf{CHURI}, a detection-gated architecture for resource-constrained retail. CHURI employs a three-stage cascade: (i) lightweight monitoring via zone-BGS at 2\,FPS to detect motion, (ii) gated verification via YOLO with an 8\% invocation rate to confirm persons, and (iii) persistent tracking via contour-velocity propagation to sustain trajectories for 50+ frames without re-detection. Key innovations include (i) zone-adaptive background subtraction (16$\times$16 grid, $\sim$6.1KB state) with dual learning rates, (ii) occupancy-aware updates to prevent stationary shoppers from background absorption, and (iii) oscillation-aware noise suppression to throttle unstable zones. CHURI achieves 78.2\% recall on WEPDTOF overhead surveillance (+26.1pp over YOLO; +93\% over MOG2), 87.6\% on MERL Shopping, and 92.1\% on Innovatiana Shoplifting, enabling Raspberry Pi 5 deployment at $\sim$1/70th traditional infrastructure cost.

\end{abstract}

\begin{figure}[t]
\centering
\includegraphics[width=\linewidth]{figures/dataset}
\caption{Evaluation datasets spanning diverse retail surveillance scenarios: (left) Innovatiana Shoplifting Dataset with behavioral anomaly monitoring in office/retail settings, (center) MERL Shopping Dataset with frontal perspective checkout scenarios, (right) WEPDTOF Dataset with overhead fisheye retail environments demonstrating viewpoint diversity.}
\label{fig:datasets}
\end{figure}


% \begin{figure}[t]
% \centering
% \includegraphics[width=\linewidth]{figures/dataset.png}
% \caption{Evaluation datasets spanning diverse retail surveillance scenarios: (left) Innovatiana Shoplifting Dataset with behavioral anomaly monitoring in office/retail settings, (center) MERL Shopping Dataset with frontal perspective checkout scenarios, (right) WEPDTOF Dataset with overhead fisheye retail environments demonstrating viewpoint diversity.}
% \label{fig:datasets}
% \end{figure}
