2021
Santos, F.; Durães, D.; Marcondes, F. S.; Lange, S.; Machado, J.; Novais, P.
Efficient Violence Detection Using Transfer Learning Proceedings Article
Em: F., Duraes D. El Bolock A. De La Prieta (Ed.): pp. 65-75, Springer Science and Business Media Deutschland GmbH, 2021, ISSN: 18650929, (cited By 4; Conference of International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2021 ; Conference Date: 6 October 2021 Through 9 October 2021; Conference Code:266119).
Resumo | Links | BibTeX | Etiquetas: Action recognition; Activity recognition; Deep learning; Fine tuning; General patterns; Learn+; State of the art; Transfer learning; Violence detections; Violent behavior, Deep learning, Human computer interaction; Pattern recognition; Security systems
@inproceedings{Santos202165,
title = {Efficient Violence Detection Using Transfer Learning},
author = {F. Santos and D. Durães and F. S. Marcondes and S. Lange and J. Machado and P. Novais},
editor = {Duraes D. El Bolock A. De La Prieta F.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116505876&doi=10.1007%2f978-3-030-85710-3_6&partnerID=40&md5=c229aac41b0bc0f030eb19d748792d78},
doi = {10.1007/978-3-030-85710-3_6},
issn = {18650929},
year = {2021},
date = {2021-01-01},
journal = {Communications in Computer and Information Science},
volume = {1472 CCIS},
pages = {65-75},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {In recent years several applications, namely in surveillance, human-computer interaction and video recovery based on its content has studied the detection and recognition of violence [22]. The purpose of violence detection is to automatically and effectively determine whether or not violence occurs in a short time. So, it is a crucial area since it will automatically enable the necessary means to stop the violence. To quickly solve this problem, we used models trained to solve general activity recognition problems such as Kinetics-400 to learn to extract general patterns that are very important to detect violent behaviour in videos. Our approach consists of using a state of the art pre-trained model in general activity recognition tasks (e.g. Kinetics-400) and then fine-tuning it to violence detection. We applied this approach in two violence datasets and achieved state-of-the-art results using only four input frames. © 2021, Springer Nature Switzerland AG.},
note = {cited By 4; Conference of International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2021 ; Conference Date: 6 October 2021 Through 9 October 2021; Conference Code:266119},
keywords = {Action recognition; Activity recognition; Deep learning; Fine tuning; General patterns; Learn+; State of the art; Transfer learning; Violence detections; Violent behavior, Deep learning, Human computer interaction; Pattern recognition; Security systems},
pubstate = {published},
tppubtype = {inproceedings}
}