Print Email Facebook Twitter Radar and Video Multimodal Learning for Human Activity Classification Title Radar and Video Multimodal Learning for Human Activity Classification Author de Jong, R.J. Heiligers, M.J.C. de Wit, J.J.M. Uysal, F. Publication year 2019 Abstract Camera systems are widely used for surveillance in the security and defense domains. The main advantages of camera systems are their high resolution, their ease of use, and the fact that optical imagery is easy to interpret for human operators. However, particularly when considering application in the defense domain, cameras have some disadvantages. In poor lighting conditions, dust or smoke the image quality degrades and, additionally, cameras cannot provide range information. These issues may be alleviated by exploiting the strongpoints of radar. Radar performance is largely preserved during nighttime, in varying weather conditions and in dust and smoke. Furthermore, radar provides range information of detected objects. Since their qualities appear to be complementary, can radar and camera systems learn from each other? In the current study, the potential of radar/video multimodal learning is assessed for the classification of human activity. Subject Human activity classificationMicro-DopplerVideoCamerasDustNetwork securityObject detectionRadar imagingSecurity systemsSmokeHigh resolutionHuman operatorLighting conditionsMulti-modal learningOptical imageryRadar performanceRange informationRadar To reference this document use: http://resolver.tudelft.nl/uuid:6e2dec8c-df92-454a-ba72-26500df67262 DOI https://doi.org/10.1109/radar41533.2019.171283 TNO identifier 876242 Publisher Institute of Electrical and Electronics Engineers IEEE ISBN 9781728126609 Source International Radar Conference, RADAR 2019, 23-27 September 2019, Toulon, France Article number 9078892 Document type conference paper Files To receive the publication files, please send an e-mail request to TNO Library.