系统梳理DNN视频分析的高效优化技术，涵盖硬件到部署全链路。
A Survey on Efficiency Optimization Techniques for DNN-based Video Analytics: Process Systems, Algorithms, and Applications
- 从硬件、数据处理到部署，分层整理效率优化方法。
- 对比现有技术在延迟、能耗等指标上的表现差异。
- 适合研究视频分析系统性能优化的学者与工程师。
近年来视频数据的爆炸式增长对视频分析提出了更高要求，准确性和效率成为两大核心关注点。深度神经网络（DNN）被广泛用于保障准确性，但其在视频分析中的效率提升仍是开放挑战。不同于以往主要从准确性角度综述DNN视频分析的研究，本文旨在全面回顾聚焦于提升DNN在视频分析中效率的优化技术。我们采用自底向上的组织方式，涵盖硬件支持、数据处理、运行部署等多个视角。最后，基于优化框架和现有工作，分析并讨论了DNN视频分析性能优化中的关键问题与挑战。
原文摘要 · Abstract (English)
The explosive growth of video data in recent years has brought higher demands for video analytics, where accuracy and efficiency remain the two primary concerns. Deep neural networks (DNNs) have been widely adopted to ensure accuracy; however, improving their efficiency in video analytics remains an open challenge. Different from existing surveys that make summaries of DNN-based video mainly from the accuracy optimization aspect, in this survey, we aim to provide a thorough review of optimization techniques focusing on the improvement of the efficiency of DNNs in video analytics. We organize existing methods in a bottom-up manner, covering multiple perspectives such as hardware support, data processing, operational deployment, etc. Finally, based on the optimization framework and existing works, we analyze and discuss the problems and challenges in the performance optimization of DNN-based video analytics.
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