# Video Analytics on IoT devices

arXiv: https://arxiv.org/abs/2102.07455 · HTML: https://mlsanity.com/paper/2102.07455/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 15 Feb 2021.

## Builds on (most cited first, 4 of 4)

- [Deep Learning vs. Traditional Computer Vision](https://mlsanity.com/paper/1910.13796/) (2019)
- [Image Matching Using SIFT, SURF, BRIEF and ORB: Performance Comparison for Distorted Images](https://mlsanity.com/paper/1710.02726/) (2017)
- [Image Identification Using SIFT Algorithm: Performance Analysis against Different Image Deformations](https://mlsanity.com/paper/1710.02728/) (2017)
- [A Microphone Array and Voice Algorithm based Smart Hearing Aid](https://mlsanity.com/paper/1908.07324/) (2019)

## Built on by (most cited first, 1 of 1)

- [A Survey of Performance Optimization in Neural Network-Based Video Analytics Systems](https://mlsanity.com/paper/2105.14195/) (2021)

## Abstract (arXiv)

Deep Learning (DL) combined with advanced model optimization methods such as RC-NN and Edge2Train has enabled offline execution of large networks on the IoT devices. In this paper, we compare the modern Deep Learning (DL) based video analytics approaches with the standard Computer Vision (CV) based approaches and finally, discuss the best-suited approach for video analytics on IoT devices.