A Survey of Performance Optimization in Neural Network-Based Video Analytics Systems

TL;DR

This survey reviews techniques for optimizing the performance of neural network-based video analytics systems, which are crucial for processing large-scale video data efficiently in tasks like object detection and video annotation.

Contribution

It provides a comprehensive overview of performance optimization methods specifically tailored for neural network-based video analytics, filling a gap left by previous surveys focused on accuracy.

Findings

Summarizes key performance optimization techniques

Highlights challenges and future directions in the field

Provides a comparative analysis of existing methods

Abstract

Video analytics systems perform automatic events, movements, and actions recognition in a video and make it possible to execute queries on the video. As a result of a large number of video data that need to be processed, optimizing the performance of video analytics systems has become an important research topic. Neural networks are the state-of-the-art for performing video analytics tasks such as video annotation and object detection. Prior survey papers consider application-specific video analytics techniques that improve accuracy of the results; however, in this survey paper, we provide a review of the techniques that focus on optimizing the performance of Neural Network-Based Video Analytics Systems.