\section{Introduction}
\label{sec:intro}

Person tracking in small retail serves three critical functions: (i) theft deterrence through real-time occupancy alerts (stores with visible counting experience 23\% lower shrinkage), (ii) regulatory compliance for fire code occupancy limits, and (iii) queue management during peak hours~\cite{hameed2025comprehensive}. Unlike warehouse surveillance, where post-incident forensics suffice, retail tracking requires real-time inference; alerts must trigger within 2-3 seconds to enable staff intervention. This real-time constraint, combined with multi-camera coverage (4-8 cameras for typical 800-1200 sq ft stores), creates deployment challenges: existing solutions either assume cloud connectivity with per-camera subscriptions or require on-premises GPU infrastructure, both exceeding small-store budgets by $5\text{-}10\times$.

Overhead fisheye cameras are commonly used in small retail installations for three primary reasons~\cite{gochoo2023fisheye8k}. \textit{First}, 8-12 foot ceiling mounts with downward orientation minimize face capture, addressing GDPR Article 35 privacy requirements. \textit{Second}, a 180-360$^\circ$~field of view enables single-camera aisle coverage vs 3-4 narrow-FOV cameras, reducing cabling costs. \textit{Third}, ceiling mounts resist customer tampering; ground-level cameras experience three times higher vandalism rates. However, this geometry creates a detector performance gap: COCO-pretrained models achieve 89\% AP on frontal pedestrian views but degrade to 52.1\% recall on overhead perspectives, as training data emphasizes horizontal aspect ratios while top-down views compress people to near-square \cite{tsai2022mobilenetjde, liu2023sparsetrack, kim2021pqk}.

On-device processing offers advantages beyond cost. GDPR Right to Erasure (Article 17) is simplified when video never leaves the premises; however, cloud storage creates 30-90 day retention obligations. Network latency for cloud round-trip (camera $\rightarrow$ upload $\rightarrow$ inference $\rightarrow$ alert) averages 800-1200ms vs edge processing at 150-250ms, missing theft intervention windows. Offline resilience matters: 47\% of small stores experience internet outages 2-4 times monthly, rendering cloud-dependent systems non-functional. However, Raspberry Pi 5 (quad-core 2.4 GHz, 8GB RAM), an upper bound of affordable edge devices (\$80 vs \$8,000 GPU workstations), saturates CPU at 82\% for single-camera YOLO inference, leaving no multi-camera headroom~\cite{wang2024yolov10, li2023edgeyolo}.

Classical tracking pipelines assume either continuous detection (ByteTrack, FairMOT) or standalone background subtraction (MOG2, KNN)~\cite{cioppa2020asynchronous}. The former achieves 92\% MOTA on MOT17 but requires GPU inference at 10-30 FPS. The latter enables edge deployment but suffers from catastrophic drift, i.e., stationary people are absorbed into background models within 100 frames (8.3 seconds at 12 FPS), fragmenting tracks~\cite{qin2023motiontrack}. Hybrid approaches invoking detectors every N frames reduce inference load but cause 35-48\% ID switch rates. The core challenge: how can tracks persist across 10-30 frame gaps without classical BGS drift?

We introduce \textbf{\textit{CHURI}}, which employs zone-level background subtraction (a $16\times16$ grid with 6.1 KB of memory per camera, compared to 37.4 MB per pixel) with dual learning rates conditioned on YOLO-derived occupancy masks. Zones overlapping person detections use slow learning (LR=0.0003) to prevent stationary absorption, while background zones adapt rapidly (LR=0.5). Sticky tracking sustains trajectories for 50+ frames from single YOLO confirmation through contour-velocity propagation, enabling 78.2\% recall on WEPDTOF overhead surveillance, which is a 50\% improvement over standalone YOLO (52.1\%) and 93\% over MOG2 (40.5\%).

We validate \textit{CHURI} across three datasets: WEPDTOF (16 overhead fisheye videos, 1,687 frames), MERL Shopping (106 perspective camera clips), and Innovatiana Shoplifting (high-resolution behavioral anomalies). CHURI achieves 78.2\%, 87.6\%, and 92.1\% recall, respectively, enabling 8-camera deployment on a single Raspberry Pi 5 at 2 FPS per camera with 129\% CPU utilization.

Our contributions are threefold. \textit{First}, we demonstrate that zone-level background subtraction achieves 78.2\% recall through occupancy-aware dual learning rates, while consuming 6.1 KB per camera, which is 6,131 times less than per-pixel methods. \textit{Second}, we introduce gated YOLO verification at an 8\% invocation rate, combined with sticky tracking, which reduces the detector budget by $12\times$ while sustaining tracks through 50+ frame gaps. \textit{Third}, we validate an 8-camera edge deployment at 1/70th the infrastructure cost of cloud subscriptions (\$350 hardware vs. \$3,840/year), while maintaining offline operation and GDPR-compliant on-device processing.
