Multi-Modal Cross Learning for an FMCW Radar Assisted by Thermal and RGB Cameras to Monitor Gestures and Cooking Processes
This paper proposes a multi-modal cross learning approach to augment the neural network training phase by additional sensor data. The approach is multi-modal during training (i.e., radar Range-Doppler maps, thermal camera images, and RGB camera images are used for training). In inference, the approa...
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| Published in: | IEEE access Vol. 9; pp. 22295 - 22303 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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IEEE
2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2169-3536, 2169-3536 |
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| Abstract | This paper proposes a multi-modal cross learning approach to augment the neural network training phase by additional sensor data. The approach is multi-modal during training (i.e., radar Range-Doppler maps, thermal camera images, and RGB camera images are used for training). In inference, the approach is single-modal (i.e., only radar Range-Doppler maps are needed for classification). The proposed approach uses a multi-modal autoencoder training which creates a compressed data representation containing correlated features across modalities. The encoder part is then used as a pretrained network for the classification task. The benefits are that expensive sensors like high resolution thermal cameras are not needed in the application but a higher classification accuracy is achieved because of the multi-modal cross learning during training. The autoencoders can also be used to generate hallucinated data of the absent sensors. The hallucinated data can be used for user interfaces, a further classification, or other tasks. The proposed approach is verified within a simultaneous cooking process classification, <inline-formula> <tex-math notation="LaTeX">2\times 2 </tex-math></inline-formula> cooktop occupancy detection, and gesture recognition task. The main functionality is an overboil protection and gesture control of a <inline-formula> <tex-math notation="LaTeX">2\times 2 </tex-math></inline-formula> cooktop. The multi-modal cross learning approach considerably outperforms single-modal approaches on that challenging classification task. |
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| AbstractList | This paper proposes a multi-modal cross learning approach to augment the neural network training phase by additional sensor data. The approach is multi-modal during training (i.e., radar Range-Doppler maps, thermal camera images, and RGB camera images are used for training). In inference, the approach is single-modal (i.e., only radar Range-Doppler maps are needed for classification). The proposed approach uses a multi-modal autoencoder training which creates a compressed data representation containing correlated features across modalities. The encoder part is then used as a pretrained network for the classification task. The benefits are that expensive sensors like high resolution thermal cameras are not needed in the application but a higher classification accuracy is achieved because of the multi-modal cross learning during training. The autoencoders can also be used to generate hallucinated data of the absent sensors. The hallucinated data can be used for user interfaces, a further classification, or other tasks. The proposed approach is verified within a simultaneous cooking process classification, [Formula Omitted] cooktop occupancy detection, and gesture recognition task. The main functionality is an overboil protection and gesture control of a [Formula Omitted] cooktop. The multi-modal cross learning approach considerably outperforms single-modal approaches on that challenging classification task. This paper proposes a multi-modal cross learning approach to augment the neural network training phase by additional sensor data. The approach is multi-modal during training (i.e., radar Range-Doppler maps, thermal camera images, and RGB camera images are used for training). In inference, the approach is single-modal (i.e., only radar Range-Doppler maps are needed for classification). The proposed approach uses a multi-modal autoencoder training which creates a compressed data representation containing correlated features across modalities. The encoder part is then used as a pretrained network for the classification task. The benefits are that expensive sensors like high resolution thermal cameras are not needed in the application but a higher classification accuracy is achieved because of the multi-modal cross learning during training. The autoencoders can also be used to generate hallucinated data of the absent sensors. The hallucinated data can be used for user interfaces, a further classification, or other tasks. The proposed approach is verified within a simultaneous cooking process classification, <inline-formula> <tex-math notation="LaTeX">2\times 2 </tex-math></inline-formula> cooktop occupancy detection, and gesture recognition task. The main functionality is an overboil protection and gesture control of a <inline-formula> <tex-math notation="LaTeX">2\times 2 </tex-math></inline-formula> cooktop. The multi-modal cross learning approach considerably outperforms single-modal approaches on that challenging classification task. This paper proposes a multi-modal cross learning approach to augment the neural network training phase by additional sensor data. The approach is multi-modal during training (i.e., radar Range-Doppler maps, thermal camera images, and RGB camera images are used for training). In inference, the approach is single-modal (i.e., only radar Range-Doppler maps are needed for classification). The proposed approach uses a multi-modal autoencoder training which creates a compressed data representation containing correlated features across modalities. The encoder part is then used as a pretrained network for the classification task. The benefits are that expensive sensors like high resolution thermal cameras are not needed in the application but a higher classification accuracy is achieved because of the multi-modal cross learning during training. The autoencoders can also be used to generate hallucinated data of the absent sensors. The hallucinated data can be used for user interfaces, a further classification, or other tasks. The proposed approach is verified within a simultaneous cooking process classification, 2 × 2 cooktop occupancy detection, and gesture recognition task. The main functionality is an overboil protection and gesture control of a 2 × 2 cooktop. The multi-modal cross learning approach considerably outperforms single-modal approaches on that challenging classification task. |
| Author | Ott, Peter Stache, Nicolaj C. Altmann, Marco Waldschmidt, Christian |
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| SubjectTerms | autoencoder Cameras Classification Coders Cooking cross learning Decoding Gesture recognition Learning Machine learning modality hallucination multimodal sensors Neural networks Occupancy Radar radar applications Radar imaging Radar range range-doppler Sensors Task analysis thermal camera Training User interfaces |
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| Title | Multi-Modal Cross Learning for an FMCW Radar Assisted by Thermal and RGB Cameras to Monitor Gestures and Cooking Processes |
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