Identity-Aware Attribute Recognition via Real-Time Distributed Inference in Mobile Edge Clouds
Zichuan Xu, Jiangkai Wu, Qiufen Xia, Pan Zhou, Jiankang Ren, Huizhi, Liang

TL;DR
This paper introduces a novel distributed inference framework for real-time pedestrian attribute recognition and re-identification in mobile edge cloud environments, significantly reducing delay and maintaining high accuracy.
Contribution
It proposes a new distributed inference model and a learning-based algorithm tailored for MEC-enabled camera networks, addressing real-time constraints and network uncertainties.
Findings
Achieved attribute recognition accuracy of 92.9%.
Reached person re-ID accuracy of 96.6%.
Reduced inference delay by at least 40.6%.
Abstract
With the development of deep learning technologies, attribute recognition and person re-identification (re-ID) have attracted extensive attention and achieved continuous improvement via executing computing-intensive deep neural networks in cloud datacenters. However, the datacenter deployment cannot meet the real-time requirement of attribute recognition and person re-ID, due to the prohibitive delay of backhaul networks and large data transmissions from cameras to datacenters. A feasible solution thus is to employ mobile edge clouds (MEC) within the proximity of cameras and enable distributed inference. In this paper, we design novel models for pedestrian attribute recognition with re-ID in an MEC-enabled camera monitoring system. We also investigate the problem of distributed inference in the MEC-enabled camera network. To this end, we first propose a novel inference framework with a…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · Advanced Neural Network Applications · Fire Detection and Safety Systems
