Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/85724
Título: | Fusion object detection and action recognition to predict violent action |
Autor(es): | Rodrigues, Nelson Ricardo Pereira Costa, Nuno Miguel Cerqueira Melo, César Gonçalo Macedo Abbasi, Ali Fonseca, Jaime C. Cardoso, Paulo Borges, João |
Palavras-chave: | Machine learning Visual intelligence Object detection Image processing Action recognition Autonomous vehicles |
Data: | 15-Jun-2023 |
Editora: | Multidisciplinary Digital Publishing Institute (MDPI) |
Revista: | Sensors |
Citação: | Rodrigues, N.R.P.; da Costa, N.M.C.; Melo, C.; Abbasi, A.; Fonseca, J.C.; Cardoso, P.; Borges, J. Fusion Object Detection and Action Recognition to Predict Violent Action. Sensors 2023, 23, 5610. https://doi.org/10.3390/s23125610 |
Resumo(s): | In the context of Shared Autonomous Vehicles, the need to monitor the environment inside the car will be crucial. This article focuses on the application of deep learning algorithms to present a fusion monitoring solution which was three different algorithms: a violent action detection system, which recognizes violent behaviors between passengers, a violent object detection system, and a lost items detection system. Public datasets were used for object detection algorithms (COCO and TAO) to train state-of-the-art algorithms such as YOLOv5. For violent action detection, the MoLa InCar dataset was used to train on state-of-the-art algorithms such as I3D, R(2+1)D, SlowFast, TSN, and TSM. Finally, an embedded automotive solution was used to demonstrate that both methods are running in real-time. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/85724 |
DOI: | 10.3390/s23125610 |
ISSN: | 1424-8220 |
e-ISSN: | 1424-8220 |
Versão da editora: | https://www.mdpi.com/1424-8220/23/12/5610 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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sensors-23-05610.pdf | 3,55 MB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons