Show simple item record

AuthorHimeur, Yassine
AuthorAl-Maadeed, Somaya
AuthorKheddar, Hamza
AuthorAl-Maadeed, Noor
AuthorAbualsaud, Khalid
AuthorMohamed, Amr
AuthorKhattab, Tamer
Available date2023-02-23T09:13:05Z
Publication Date2023
Publication NameEngineering Applications of Artificial Intelligence
ResourceScopus
URIhttp://dx.doi.org/10.1016/j.engappai.2022.105698
URIhttp://hdl.handle.net/10576/40341
AbstractRecently, 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.
SponsorThis research work was made possible by research grant support (QUEX-CENG-SCDL-19/20-1) from Supreme Committee for Delivery and Legacy (SC) in Qatar. The statements made herein are solely the responsibility of the authors. Open Access funding provided by the Qatar National Library.
Languageen
PublisherElsevier
SubjectDeep domain adaptation
Deep learning
Deep transfer learning
Fine-tuning
Video surveillance
TitleVideo surveillance using deep transfer learning and deep domain adaptation: Towards better generalization
TypeOther
Pagination-
Volume Number119
dc.accessType Open Access


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record