YOLOpeds: Efficient Real-Time Single-Shot Pedestrian Detection for Smart Camera Applications
Christos Kyrkou

TL;DR
YOLOpeds is a novel, efficient deep learning model for real-time pedestrian detection on resource-constrained smart cameras, achieving high accuracy and speed suitable for various surveillance and autonomous applications.
Contribution
This work introduces YOLOpeds, a lightweight neural network architecture with multi-scale features and an anchor-less detection approach for improved efficiency and accuracy.
Findings
Real-time detection at over 30 fps
Detection accuracy of approximately 86% on PETS2009 dataset
Outperforms existing deep learning pedestrian detectors
Abstract
Deep Learning-based object detectors can enhance the capabilities of smart camera systems in a wide spectrum of machine vision applications including video surveillance, autonomous driving, robots and drones, smart factory, and health monitoring. Pedestrian detection plays a key role in all these applications and deep learning can be used to construct accurate state-of-the-art detectors. However, such complex paradigms do not scale easily and are not traditionally implemented in resource-constrained smart cameras for on-device processing which offers significant advantages in situations when real-time monitoring and robustness are vital. Efficient neural networks can not only enable mobile applications and on-device experiences but can also be a key enabler of privacy and security allowing a user to gain the benefits of neural networks without needing to send their data to the server to…
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