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arXiv Computer Vision By Mayank Atreya, Navin Chhibber, Shilpa Ajay, R Loshini, K Anitha

Real-Time Smart Retail Analytics through Fog Computing using the RetailLightNet Lightweight Model

Read the original on arXiv Computer Vision →

The paper introduces Fog-based RetailLightNet (F RLN), an ultra‑lightweight deep learning model designed for real‑time video analytics in smart retail environments. By deploying the model on edge devices such as the NVIDIA Jetson Nano and using motion‑gated frame selection, the system reduces latency and computational load while maintaining high precision (0.988), recall (0.976), and F1‑score (0.976). The approach leverages publicly available retail datasets (SKU110K and RPC) and depth‑wise separable convolutions to enable efficient shelf monitoring and queue assessment.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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