Abstract
Smart retail systems now depend on the application of real-time video analytics, which can track customer behavior, manage shelf inventory, and enhance in-store processes. Currently cloud-based applications of video analytics face multiple challenges such as high latency of the communication itself, privacy concerns surrounding transmitting raw video, and the computational demands of the heavy vision models used on the often limited network infrastructure. These challenges lead to delays in decision making where prompt action is crucial. To address these short comings, this research proposes Fog-based analytics withRetailLightNet (F RLN), an ultra lightweight deep learning model which is specifically suited for the edge, resource constrained hardware capabilities of devices like the NVIDIA Jetson Nano. The approach utilizes publicly available retail datasets, SKU110K, and RPC, which contains an assortment of product, shelf, and customer interaction scenarios for this http URL processes include preprocessing images through normalization and employing motion-gated frame selection to improve efficiency by omitting redundant frames to ease the workload on the fog processing. Internal within Retail LightNet, image feature extraction is realized through depth wise separable convolutions which help support spatial and channel-wise learning without heavy computational costs. The model employs two prediction heads presence classification, and density regression, supporting shelf monitoring and queue assessment. The proposed F RLN framework attained better results in terms of precision (0.988), recall (0.976), and F1-score(0.976) when compared with existing convolutional neural network. Keywords: fog computing, motion-gated frame selection,NVIDIA jetson, retail lightnet, smart retail.
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URL
https://arxiv.org/abs/2610.35871
