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.
@article{arxiv.2105.14195,
title = {A Survey of Performance Optimization in Neural Network-Based Video Analytics Systems},
author = {Nada Ibrahim and Preeti Maurya and Omid Jafari and Parth Nagarkar},
journal= {arXiv preprint arXiv:2105.14195},
year = {2021}
}