Snippet based trajectory statistics histograms for assistive technologies
dc.citation.epage | 16 | en_US |
dc.citation.spage | 3 | en_US |
dc.contributor.author | İscen, Ahmet | en_US |
dc.contributor.author | Wang Y. | en_US |
dc.contributor.author | Duygulu, Pınar | en_US |
dc.contributor.author | Hauptmann, A. | en_US |
dc.coverage.spatial | Zurich, Switzerland | |
dc.date.accessioned | 2016-02-08T12:12:32Z | |
dc.date.available | 2016-02-08T12:12:32Z | |
dc.date.issued | 2014-09 | en_US |
dc.department | Department of Computer Engineering | en_US |
dc.description | Date of Conference: 6-7 and 12 September, 2014 | |
dc.description | Conference name: European Conference on Computer Vision. ECCV 2014: Computer Vision - ECCV 2014 Workshops | |
dc.description.abstract | Due to increasing hospital costs and traveling time, more and more patients decide to use medical devices at home without traveling to the hospital. However, these devices are not always very straight-forward for usage, and the recent reports show that there are many injuries and even deaths caused by the wrong use of these devices. Since human supervision during every usage is impractical, there is a need for computer vision systems that would recognize actions and detect if the patient has done something wrong. In this paper, we propose to use Snippet Based Trajectory Statistics Histograms descriptor to recognize actions in two medical device usage problems; inhaler device usage and infusion pump usage. Snippet Based Trajectory Statistics Histograms encodes the motion and position statistics of densely extracted trajectories from a video. Our experiments show that by using Snippet Based Trajectory Statistics Histograms technique, we improve the overall performance for both tasks. Additionally, this method does not require heavy computation, and is suitable for real-time systems. © Springer International Publishing Switzerland 2015. | en_US |
dc.description.provenance | Made available in DSpace on 2016-02-08T12:12:32Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2015 | en |
dc.identifier.doi | 10.1007/978-3-319-16220-1_1 | en_US |
dc.identifier.uri | http://hdl.handle.net/11693/28152 | en_US |
dc.language.iso | English | en_US |
dc.publisher | Springer | en_US |
dc.relation.isversionof | https://doi.org/10.1007/978-3-319-16220-1_1 | en_US |
dc.source.title | European Conference on Computer Vision ECCV 2014: Computer Vision - ECCV 2014 Workshops | en_US |
dc.subject | Action recognition | en_US |
dc.subject | Assisted living systems | en_US |
dc.subject | Medical device usage | en_US |
dc.subject | Biomedical equipment | en_US |
dc.subject | Computer vision | en_US |
dc.subject | Graphic methods | en_US |
dc.subject | Hospitals | en_US |
dc.subject | Interactive computer systems | en_US |
dc.subject | Medical computing | en_US |
dc.subject | Medical problems | en_US |
dc.subject | Statistics | en_US |
dc.subject | Trajectories | en_US |
dc.subject | Action recognition | en_US |
dc.subject | Assisted living | en_US |
dc.subject | Assistive technology | en_US |
dc.subject | Computer vision system | en_US |
dc.subject | Human supervision | en_US |
dc.subject | Medical Devices | en_US |
dc.subject | Statistics histograms | en_US |
dc.subject | Traveling time | en_US |
dc.subject | Real time systems | en_US |
dc.title | Snippet based trajectory statistics histograms for assistive technologies | en_US |
dc.type | Conference Paper | en_US |
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