\section{Related Work}
\label{sec:related}

\textbf{Multi-Object Tracking (MOT).}
Tracking-by-detection pipelines (e.g., SORT~\cite{bewley2016sort}) are attractive for edge due to lightweight association (Kalman + Hungarian; reported 260\,Hz CPU throughput), but they assume \emph{continuous} detections at 10-30\,FPS. In our setting, invoking YOLO every frame saturates a Raspberry Pi 5 at 82\% CPU for \emph{one} camera, leaving no headroom for multi-camera deployments. DeepSORT~\cite{wojke2017deepsort} improves re-identification via appearance features and reports Raspberry Pi + NCS2 feasibility, yet multi-camera scaling is dominated by per-camera replication: $\sim$1.2\,GB per instance implies $1.2\,\text{GB}\times 8=9.6$\,GB, exceeding an 8\,GB RPi5. ByteTrack~\cite{zhang2022bytetrack} and OC-SORT~\cite{cao2023ocsort} improve association under missed detections/occlusions, but have limited validation for overhead fisheye retail (e.g., WEPDTOF) and still inherit the need for frequent detections; ByteTrack reports only $\sim$3\,FPS on Jetson Orin for retail scenarios. Stronger variants (BoT-SORT~\cite{aharon2022botsort}, StrongSORT~\cite{du2023strongsort}) and transformer-based trackers (MOTR~\cite{zhu2020motr}, FairMOT~\cite{zhang2021fairmot}) achieve strong MOT17/MOT20 performance but rely on GPU-class inference. Similarly, RT-DETR~\cite{lv2024rtdetr} reaches high AP on GPUs (e.g., 30\,FPS on A100) yet drops to $<1$\,FPS on ARM, making always-on detection infeasible.

\textbf{Background Subtraction (BGS) on Edge.}
Classical BGS enables multi-camera operation due to low memory/compute, but degrades without detector verification. MOG2~\cite{zivkovic2004mog2} consumes 94\,MB per camera (8 cameras = 299\,MB RAM, versus 9.6\,GB for 8 DeepSORT replicas), yet achieves only 40.5\% recall on WEPDTOF: stationary shoppers are absorbed into the background within $\sim$100 frames (8.3\,s at 12\,FPS), fragmenting tracks. KNN~\cite{zivkovic2006knn} shows similar drift (54.8\% recall). Deep BGS methods (FgSegNet~\cite{lim2018fgsegnet}, BSUV-Net) deliver 95-98\% CDnet segmentation accuracy but require GPU inference at 5-15\,FPS, exceeding Raspberry Pi budgets and precluding multi-camera deployment. Thus, classical BGS scales but drifts; deep BGS is accurate but not edge-feasible.

\textbf{Retail/Overhead Surveillance Systems.}
Fisheye-focused detectors such as PF-YOLO~\cite{chen2024pfyolo} improve overhead detection (53.1\% mAP50 on WEPDTOF) but do not address tracking continuity or multi-camera scaling. RAPiD~\cite{xu2020rapid} designs rotation-aware anchors for overhead fisheye detection (CEPDOF) but does not tackle MOT under sparse detections. Many ``edge retail'' YOLO+DeepSORT-style systems report on standard MOT datasets rather than standardized overhead benchmarks like WEPDTOF, despite strong viewpoint sensitivity (e.g., 89\% AP frontal $\rightarrow$ 52.1\% overhead).

\textbf{Edge Constraints and Sparse Detection.}
Model compression (MobileNet-SSD, YOLO-Nano) reduces parameters but often sacrifices 15-22pp recall and still requires multiple devices for store-wide coverage due to per-device overhead. Hardware accelerators (EdgeTPU, NCS2) speed inference but retain per-camera replication costs (e.g., $8\times 1.2$\,GB = 9.6\,GB). Hybrid ``detect every $N$ frames'' strategies reduce computation but increase fragmentation, yielding 35-48\% ID-switch rates when trajectories break during detector dropouts.

These gaps motivate \textit{CHURI}'s resource-first design: a zone-level background model (16$\times$16 grid, $\sim$6.1\,KB per camera) enables multi-camera deployment on a single Raspberry Pi 5, while sparse detector confirmations and sticky contour-velocity propagation maintain trajectories across 50+ frame detection gaps. This yields a 6{,}131$\times$ memory reduction versus per-pixel BGS, and improves recall (78.2\% vs. 65.9\% for per-pixel adaptive BGS on WEPDTOF) while supporting substantially higher camera density.

The fundamental insight is that multi-camera edge deployment requires a paradigm shift from accuracy-first to resource-first design. Rather than optimizing detector performance and then attempting edge compression (MobileNet, quantization), we invert the problem: what is the minimal background model sufficient for tracking continuity when fused with sparse detector confirmations? Zone-level statistics provide the answer: coarse spatial resolution (256 zones vs. 1.5M pixels) captures person-scale motion, while sticky tracking compensates for zone boundary artifacts through temporal persistence. This resource-constrained design achieves comparable accuracy to per-pixel methods (78.2\% vs 65.9\% recall on per-pixel adaptive BGS) while enabling 4$\times$ more cameras per device. Section~\ref{sec:method} details our approach.
