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arXiv · 2504.03709

Ocularone-Bench: Benchmarking DNN Models on GPUs to Assist the Visually Impaired

Suman Raj·Bhavani A Madhabhavi·Kautuk Astu·Arnav A Rajesh·Pratham M·Yogesh Simmhan
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Abstract

VIP navigation requires multiple DNN models for identification, posture analysis, and depth estimation to ensure safe mobility. Using a hazard vest as a unique identifier enhances visibility while selecting the right DNN model and computing device balances accuracy and real-time performance. We present Ocularone-Bench, which is a benchmark suite designed to address the lack of curated datasets for uniquely identifying individuals in crowded environments and the need for benchmarking DNN inference times on resource-constrained edge devices. The suite evaluates the accuracy-latency trade-offs of YOLO models retrained on this dataset and benchmarks inference times of situation awareness models across edge accelerators and high-end GPU workstations. Our study on NVIDIA Jetson devices and RTX 4090 workstation demonstrates significant improvements in detection accuracy, achieving up to 99.4% precision, while also providing insights into real-time feasibility for mobile deployment. Beyond VIP navigation, Ocularone-Bench is applicable to senior citizens, children and worker safety monitoring, and other vision-based applications.

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Suman Raj, Bhavani A Madhabhavi, Kautuk Astu, Arnav A Rajesh, Pratham M, Yogesh Simmhan. 2025-03-27. Ocularone-Bench: Benchmarking DNN Models on GPUs to Assist the Visually Impaired. https://arxiv.org/abs/2504.03709

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