You are all cordially invited to the next AMLab colloquium on Tuesday, January 12 at 16:00 in C3.163, where Albert Huizing, principle scientist at TNO, will give a talk titled “Applying the art of deep learning to radar”.
Abstract: Deep learning techniques are currently successfully applied to object recognition in images, face recognition and speech recognition by major companies such as Google, Microsoft, Facebook, Apple, and Baidu. The application of deep learning to radar is at a more embryonic level than optical and acoustical sensors, partly because of the unknown benefits of radar in machine learning communities and partly because of the lack of large radar signal databases for training deep neural networks. The similarity of radar and optical images and radar and acoustical spectrograms, and the predicted growth of radar in commercial applications such as autonomous driving and gesture recognition, indicate that there is a need for investigating the application of deep learning in radar.
This talk will give a brief introduction of the principles of radar and provide a live demonstration of a small radar system. An overview of radar applications will show the benefits of radar when compared with other sensors such as cameras and microphones. In a case study, a model-based and a feature-based approach to human motion analysis with radar will be presented. The potential of deep learning for human motion analysis with radar will be discussed and concepts for fusing the signals of radars and cameras with multimodal deep neural networks will be explored.