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

Personalized accessibility map (PAM): a novel assisted wayfinding approach for people with disabilities

Accessibility information is necessary to support the everyday mobility of people with disabilities. As a service to aid the mobility of students with disabilities, some universities and colleges provide maps with accessibility information for their campus on the Web. Other maps, while not focused specifically on students, provide information about indoor accessibility and can be extended by users. In this paper, accessibility requirements published in the literature, the criteria used in existing geo-crowdsourcing services and the data used by campus accessibility maps (which are commonly based on the ADA standards) are used to provide an optimal set of requirements for personalized accessibility map (PAM). PAM is discussed and analysed in detail, a prototype PAM developed for the University of Pittsburgh is described, and challenges and future work are highlighted. © 2014 Taylor & Francis

Human-Driven Design: A Human-Driven Approach to the Design of Technology

Part 1: Society, Social Responsibility, Ethics and ICTInternational audienceIn the midst of the many large-scale societal and technological transformations, there is a need for design approaches that respect human values and needs and are able to integrate multiple perspectives into technology design in order to work for outcomes that are interesting, feasible and sustainable in all senses of the term. For this purpose, we discuss a possible approach to the design of technology that is driven by human and social values, is collaborative in nature and reflective in terms of responsibility and ethics in the design. We call this approach ‘Human-Driven Design’ and argue that it is needed especially when designing for enabling and emerging information and communication technologies. A human-driven design approach should focus on the early phases of design, be strongly future-oriented and aim to contribute to innovation for a sustainable society and better quality of life in the future

Pedestrian network extraction from fused aerial imagery (orthoimages) and laser imagery (Lidar)

A pedestrian network is a topological map that contains the geometric relationship between pedestrian path segments (e.g., sidewalk, crosswalk, footpath), which is needed in a variety of applications, such as pedestrian navigation services. However, current pedestrian networks are not widely available. In an effort to provide an automatic means for creating pedestrian networks, this paper presents a methodology for extracting pedestrian network from aerial and laser images. The methodology consists of data preparation and four steps: object filtering, pedestrian path region extraction, pedestrian network construction, and raster to vector conversion. An experiment, using ten images, was conducted to evaluate the performance of the methodology. Evaluation results indicate that the methodology can extract sidewalk, crosswalk, footpath, and building entrances; it collects pedestrian networks with 61 percent geometrical completeness, 67.35 percent geometrical correctness, 71 percent topological completeness and 51.38 percent topological correctness. © 2013 American Society for Photogrammetry and Remote Sensing