A Survey on Efficiency Optimization Techniques for DNN-based Video Analytics: Process Systems, Algorithms, and Applications

TL;DR

This survey reviews various techniques to enhance the efficiency of DNNs in video analytics, covering hardware, data, and deployment strategies, and discusses current challenges and future directions.

Contribution

It provides a comprehensive, organized overview of efficiency optimization methods for DNN-based video analytics, unlike previous surveys focused mainly on accuracy.

Findings

Organized methods from hardware to deployment perspectives.

Identified key challenges in performance optimization.

Discussed future research directions.

Abstract

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.