4 research outputs found
Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns
We introduce Deep Thermal Imaging, a new approach for close-range automatic
recognition of materials to enhance the understanding of people and ubiquitous
technologies of their proximal environment. Our approach uses a low-cost mobile
thermal camera integrated into a smartphone to capture thermal textures. A deep
neural network classifies these textures into material types. This approach
works effectively without the need for ambient light sources or direct contact
with materials. Furthermore, the use of a deep learning network removes the
need to handcraft the set of features for different materials. We evaluated the
performance of the system by training it to recognise 32 material types in both
indoor and outdoor environments. Our approach produced recognition accuracies
above 98% in 14,860 images of 15 indoor materials and above 89% in 26,584
images of 17 outdoor materials. We conclude by discussing its potentials for
real-time use in HCI applications and future directions.Comment: Proceedings of the 2018 CHI Conference on Human Factors in Computing
System
Lightweight Material Detection for Placement-Aware Mobile Computing
Numerous methods have been proposed that allow mobile devices to determine where they are located (e.g., home or office) and in some cases, predict what activity the user is currently engaged in (e.g., walking, sitting, or driving). While useful, this sensing currently only tells part of a much richer story. To allow devices to act most appropriately to the situation they are in, it would also be very helpful to know about their placement - for example whether they are sitting on a desk, hidden in a drawer, placed in a pocket, or held in one's hand - as different device behaviors may be called for in each of these situations. In this paper, we describe a simple, small, and inexpensive multispectral optical sensor for identifying materials in proximity to a device. This information can be used in concert with e.g., location information, to estimate, for example, that the device is "sitting on the desk at home", or "in the pocket at work". This paper discusses several potential uses of this technology, as well as results from a two-part study, which indicates that this technique can detect placement at 94.4% accuracy with real-world placement sets