Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/85593
Título: | Sensor fusion approach for multiple human motion detection for indoor surveillance use-case |
Autor(es): | Abbasi, Ali Queirós, Sandro Costa, Nuno Miguel Cerqueira Fonseca, Jaime C. Borges, João |
Palavras-chave: | Neuromorphic vision sensor Multiple human motion detection and tracking Multi-modal data Sensor fusion Indoor surveillance Event-based data |
Data: | 14-Abr-2023 |
Editora: | Multidisciplinary Digital Publishing Institute |
Revista: | Sensors |
Citação: | Abbasi, A.; Queirós, S.; da Costa, N.M.C.; Fonseca, J.C.; Borges, J. Sensor Fusion Approach for Multiple Human Motion Detection for Indoor Surveillance Use-Case. Sensors 2023, 23, 3993. https://doi.org/10.3390/s23083993 |
Resumo(s): | Multi-human detection and tracking in indoor surveillance is a challenging task due to various factors such as occlusions, illumination changes, and complex human-human and human-object interactions. In this study, we address these challenges by exploring the benefits of a low-level sensor fusion approach that combines grayscale and neuromorphic vision sensor (NVS) data. We first generate a custom dataset using an NVS camera in an indoor environment. We then conduct a comprehensive study by experimenting with different image features and deep learning networks, followed by a multi-input fusion strategy to optimize our experiments with respect to overfitting. Our primary goal is to determine the best input feature types for multi-human motion detection using statistical analysis. We find that there is a significant difference between the input features of optimized backbones, with the best strategy depending on the amount of available data. Specifically, under a low-data regime, event-based frames seem to be the preferred input feature type, while higher data availability benefits the combined use of grayscale and optical flow features. Our results demonstrate the potential of sensor fusion and deep learning techniques for multi-human tracking in indoor surveillance, although it is acknowledged that further studies are needed to confirm our findings. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/85593 |
DOI: | 10.3390/s23083993 |
ISSN: | 1424-8220 |
e-ISSN: | 1424-8220 |
Versão da editora: | https://www.mdpi.com/1424-8220/23/8/3993 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
---|---|---|---|---|
sensors-23-03993.pdf | 2,62 MB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons