Ocularone-Bench: Benchmarking DNN Models on GPUs to Assist the Visually Impaired
Journal:
arXiv
Published Date:
Mar 27, 2025
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.
