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Video surveillance using deep transfer learning and deep domain adaptation: Towards better generalization

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submitted on 2023-11-05, 11:09 and posted on 2023-11-05, 11:21 authored by Yassine Himeur, Somaya Al-Maadeed, Hamza Kheddar, Noor Al-Maadeed, Khalid Abualsaud, Amr Mohamed, Tamer Khattab

Recently, developing automated video surveillance systems (VSSs) has become crucial to ensure the security and safety of the population, especially during events involving large crowds, such as sporting events. While artificial intelligence (AI) smooths the path of computers to think like humans, machine learning (ML) and deep learning (DL) pave the way more, even by adding training and learning components. DL algorithms require data labeling and high-performance computers to effectively analyze and understand surveillance data recorded from fixed or mobile cameras installed in indoor or outdoor environments. However, they might not perform as expected, take much time in training, or not have enough input data to generalize well. To that end, deep transfer learning (DTL) and deep domain adaptation (DDA) have recently been proposed as promising solutions to alleviate these issues. Typically, they can (i) ease the training process, (ii) improve the generalizability of ML and DL models, and (iii) overcome data scarcity problems by transferring knowledge from one domain to another or from one task to another. Although the increasing number of articles proposed to develop DTL- and DDA-based VSSs, a thorough review that summarizes and criticizes the state-of-the-art is still missing. To that end, this paper introduces, to the best of the authors’ knowledge, the first overview of existing DTL- and DDA-based video surveillance to (i) shed light on their benefits, (ii) discuss their challenges, and (iii) highlight their future perspectives.

Other Information

Published in: Engineering Applications of Artificial Intelligence
License: http://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.1016/j.engappai.2022.105698

Funding

Open Access funding provided by the Qatar National Library

History

Language

  • English

Publisher

Elsevier

Publication Year

  • 2023

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License

Institution affiliated with

  • Qatar University
  • College of Engineering - QU