mlsanityData to 14 Oct 2026

Video Analytics on IoT devices

arXiv 2102.07455 ↗First posted 15 Feb 2021cs.CVcited by 1

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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.

Key facts. Cited by 1 paper 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

4 of 12 references.

References · 4

  1. 2019Deep Learning vs. Traditional Computer Visioncited by 94
  2. 2017Image Matching Using SIFT, SURF, BRIEF and ORB: Performance Comparison for Distorted Imagescited by 30
  3. 2017Image Identification Using SIFT Algorithm: Performance Analysis against Different Image Deformationscited by 3
  4. 2019A Microphone Array and Voice Algorithm based Smart Hearing Aidcited by 2

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Cited by year

Citing papers per year: 2021: 1.

1 paper.

  1. 2021A Survey of Performance Optimization in Neural Network-Based Video Analytics Systemscited by 0