基于深度学习的嵌入式目标追踪研究进展

Research Progress of Object Tracking by Deep Learning in Embedded System

作为计算机视觉领域的基本问题之一,目标追踪具有广泛的应用场景.随着硬件算力和深度学习方法的进步,常规的深度学习目标追踪方法精度越来越高,但其模型参数量庞大,计算资源和能耗需求高.近年来,随着无人机和智能物联网应用的蓬勃发展,如何在存储空间和算力有限、低功耗需求的嵌入式硬件环境中进行实时目标跟踪,成为当前研究的热点.本文对面向嵌入式应用的目标追踪方法进行了分析综述,包括相关滤波结合深度学习的目标追踪方法、基于轻量神经网络的目标跟踪方法,并总结了深度学习模型部署流程和无人机等领域的嵌入式目标追踪典型应用实例,最后对未来研究重点进行了展望.

Object tracking,a basic problem in computer vision,has a wide range of application scenarios.Due to the advance in the computational capacity of hardware and deep learning methods,conventional deep learning methods for object tracking have higher precision,but they face the problems of massive model parameters and high demand for computational resources and power consumption.In recent years,with the booming development of unmanned aerial vehicle(UAV)and Internet of Things(IoT)applications,a great deal of research focuses on how to achieve real-time tracking in embedded hardware environment with limited storage space and computational capacity and low power consumption.Firstly,object tracking algorithms in the embedded environment,including the ones combining correlation filters with deep learning and those based on lightweight neural networks,are analyzed and discussed.Secondly,deployment procedures of deep learning models and classical embedded object tracking applications,such as those in UAVs,are summarized.Finally,future research directions are given.