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With the development of deep learning technologies, attribute recognition andperson re-identification (re-ID) have attracted extensive attention andachieved continuous improvement via executing computing-intensive deep neuralnetworks in cloud datacenters. However, the datacenter deployment cannot meetthe real-time requirement of attribute recognition and person re-ID, due to theprohibitive delay of backhaul networks and large data transmissions fromcameras to datacenters. A feasible solution thus is to employ mobile edgeclouds (MEC) within the proximity of cameras and enable distributed inference.In this paper, we design novel models for pedestrian attribute recognition withre-ID in an MEC-enabled camera monitoring system. We also investigate theproblem of distributed inference in the MEC-enabled camera network. To thisend, we first propose a novel inference framework with a set of distributedmodules, by jointly considering the attribute recognition and person re-ID. Wethen devise a learning-based algorithm for the distributions of the modules ofthe proposed distributed inference framework, considering the dynamicMEC-enabled camera network with uncertainties. We finally evaluate theperformance of the proposed algorithm by both simulations with real datasetsand system implementation in a real testbed. Evaluation results show that theperformance of the proposed algorithm with distributed inference framework ispromising, by reaching the accuracies of attribute recognition and personidentification up to 92.9% and 96.6% respectively, and significantly reducingthe inference delay by at least 40.6% compared with existing methods.

Identity-Aware Attribute Recognition via Real-Time Distributed Inference in Mobile Edge Clouds