AI-Enabled sensor fusion of time of flight imaging and mmwave for concealed metal detection
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866909277358129152 |
|---|---|
| author | Kaul, Chaitanya Mitchell, Kevin J. Kassem, Khaled Tragakis, Athanasios Kapitany, Valentin Starshynov, Ilya Villa, Federica Murray-Smith, Roderick Faccio, Daniele |
| author_facet | Kaul, Chaitanya Mitchell, Kevin J. Kassem, Khaled Tragakis, Athanasios Kapitany, Valentin Starshynov, Ilya Villa, Federica Murray-Smith, Roderick Faccio, Daniele |
| contents | In the field of detection and ranging, multiple complementary sensing modalities may be used to enrich the information obtained from a dynamic scene. One application of this sensor fusion is in public security and surveillance, whose efficacy and privacy protection measures must be continually evaluated. We present a novel deployment of sensor fusion for the discrete detection of concealed metal objects on persons whilst preserving their privacy. This is achieved by coupling off-the-shelf mmWave radar and depth camera technology with a novel neural network architecture that processes the radar signals using convolutional Long Short-term Memory (LSTM) blocks and the depth signal, using convolutional operations. The combined latent features are then magnified using a deep feature magnification to learn cross-modality dependencies in the data. We further propose a decoder, based on the feature extraction and embedding block, to learn an efficient upsampling of the latent space to learn the location of the concealed object in the spatial domain through radar feature guidance. We demonstrate the detection of presence and inference of 3D location of concealed metal objects with an accuracy of up to 95%, using a technique that is robust to multiple persons. This work provides a demonstration of the potential for cost effective and portable sensor fusion, with strong opportunities for further development. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_00816 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI-Enabled sensor fusion of time of flight imaging and mmwave for concealed metal detection Kaul, Chaitanya Mitchell, Kevin J. Kassem, Khaled Tragakis, Athanasios Kapitany, Valentin Starshynov, Ilya Villa, Federica Murray-Smith, Roderick Faccio, Daniele Image and Video Processing In the field of detection and ranging, multiple complementary sensing modalities may be used to enrich the information obtained from a dynamic scene. One application of this sensor fusion is in public security and surveillance, whose efficacy and privacy protection measures must be continually evaluated. We present a novel deployment of sensor fusion for the discrete detection of concealed metal objects on persons whilst preserving their privacy. This is achieved by coupling off-the-shelf mmWave radar and depth camera technology with a novel neural network architecture that processes the radar signals using convolutional Long Short-term Memory (LSTM) blocks and the depth signal, using convolutional operations. The combined latent features are then magnified using a deep feature magnification to learn cross-modality dependencies in the data. We further propose a decoder, based on the feature extraction and embedding block, to learn an efficient upsampling of the latent space to learn the location of the concealed object in the spatial domain through radar feature guidance. We demonstrate the detection of presence and inference of 3D location of concealed metal objects with an accuracy of up to 95%, using a technique that is robust to multiple persons. This work provides a demonstration of the potential for cost effective and portable sensor fusion, with strong opportunities for further development. |
| title | AI-Enabled sensor fusion of time of flight imaging and mmwave for concealed metal detection |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2408.00816 |