Affiliation:
1. Department of Electrical and Computer Engineering Korea University Seoul Republic of Korea
Abstract
AbstractWe propose an adaptive unmanned aerial vehicle (UAV)‐assisted object recognition algorithm for urban surveillance scenarios. For UAV‐assisted surveillance, UAVs are equipped with learning‐based object recognition models and can collect surveillance image data. However, owing to the limitations of UAVs regarding power and computational resources, adaptive control must be performed accordingly. Therefore, we introduce a self‐adaptive control strategy to maximize the time‐averaged recognition performance subject to stability through a formulation based on Lyapunov optimization. Results from performance evaluations on real‐world data demonstrate that the proposed algorithm achieves the desired performance improvements.
Subject
Electrical and Electronic Engineering,General Computer Science,Electronic, Optical and Magnetic Materials
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