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

arXiv: https://arxiv.org/abs/2105.14195 · HTML: https://mlsanity.com/paper/2105.14195/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 44 references that are in the map.
- First posted on arXiv 10 May 2021.

## Builds on (most cited first, 18 of 18)

- [Human Action Recognition and Prediction: A Survey](https://mlsanity.com/paper/1806.11230/) (2018)
- [Focus: Querying Large Video Datasets with Low Latency and Low Cost](https://mlsanity.com/paper/1801.03493/) (2018)
- [BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics](https://mlsanity.com/paper/1805.01046/) (2018)
- [NoScope: Optimizing Neural Network Queries over Video at Scale](https://mlsanity.com/paper/1703.02529/) (2017)
- [A Survey on Content-Aware Video Analysis for Sports](https://mlsanity.com/paper/1703.01170/) (2017)
- [Physical Representation-based Predicate Optimization for a Visual Analytics Database](https://mlsanity.com/paper/1806.04226/) (2018)
- [Rekall: Specifying Video Events using Compositions of Spatiotemporal Labels](https://mlsanity.com/paper/1910.02993/) (2019)
- [Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics](https://mlsanity.com/paper/2007.13005/) (2020)
- [VStore: A Data Store for Analytics on Large Videos](https://mlsanity.com/paper/1810.01794/) (2018)
- [THIA: Accelerating Video Analytics using Early Inference and Fine-Grained Query Planning](https://mlsanity.com/paper/2102.08481/) (2021)
- [TASM: A Tile-Based Storage Manager for Video Analytics](https://mlsanity.com/paper/2006.02958/) (2020)
- [Visor: Privacy-Preserving Video Analytics as a Cloud Service](https://mlsanity.com/paper/2006.09628/) (2020)
- [ODIN: Automated Drift Detection and Recovery in Video Analytics](https://mlsanity.com/paper/2009.05440/) (2020)
- [AQuA: Analytical Quality Assessment for Optimizing Video Analytics Systems](https://mlsanity.com/paper/2101.09752/) (2021)
- [Scanner: Efficient Video Analysis at Scale](https://mlsanity.com/paper/1805.07339/) (2018)
- [CONVINCE: Collaborative Cross-Camera Video Analytics at the Edge](https://mlsanity.com/paper/2002.03797/) (2020)
- [ExSample: Efficient Searches on Video Repositories through Adaptive Sampling](https://mlsanity.com/paper/2005.09141/) (2020)
- [Video Analytics on IoT devices](https://mlsanity.com/paper/2102.07455/) (2021)

## Abstract (arXiv)

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.