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CamFi: An AI-driven and Camera-based System for Assisting Users in Finding Lost Objects in Multi-Person Scenarios

Published:28 April 2022Publication History

ABSTRACT

It is important to study how to help people quickly find misplaced objects. However, previous studies have focused on single-person scenarios without considering the influence of other people in public places. Based on the technology of object detection and face recognition, our system can help reduce the burden upon people's memory. It can provide useful information, whether the user forgets where the object is or because someone else has moved the object. The system includes a camera, processing server and smartphone application. To evaluate our approach, we conducted a quantitative and qualitative user study with participants (n=12). We demonstrated the usability of this system in helping users find misplaced items in public settings with multiple people.

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References

  1. Aaron Bangor, Philip Kortum, and James Miller. 2009. Determining what individual sus scores mean: adding an adjective rating scale. J. Usability Stud. 4, 3 (May 2009), 114–123.Google ScholarGoogle Scholar
  2. Alexey Bochkovskiy, Chien-Yao Wang, Hong-Yuan Mark Liao. 2020. YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv: 2004.10934. Retrieved from https://doi.org/10.48550/arXiv.2004.10934Google ScholarGoogle Scholar
  3. John Brooke. 1996. SUS: a “quick and dirty” usability scale. In P. W. Jordan, B. Thomas, B. A. Weerdmeester, & A. L. McClelland (Eds.), Usability Evaluation in Industry. Taylor and Francis.Google ScholarGoogle Scholar
  4. Andreas Butz, Michael Schneider, and Mira Spassova. 2004. SearchLight – a lightweight search function for pervasive environments. In Pervasive Computing (Lecture Notes in Computer Science). Springer, Berlin, Heidelberg, 351–356. https://doi.org/10.1007/978-3-540-24646-6_26Google ScholarGoogle Scholar
  5. Sunsern Cheamanunkul, Evan Ettinger, Matt Jacobsen, Patrick Lai, and Yoav Freund. 2009. Detecting, tracking and interacting with people in a public space. In Proceedings of the 2009 international conference on Multimodal interfaces (ICMI-MLMI ’09). Association for Computing Machinery, New York, NY, USA, 79–86. https://doi.org/10.1145/1647314.1647330Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Anooshmita Das, Krister Jens, and Mikkel Baun Kjærgaard. 2020. Space utilization and activity recognition using 3d stereo vision camera inside an educational building. In Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers (UbiComp-ISWC ’20). Association for Computing Machinery, New York, NY, USA, 629–637. https://doi.org/10.1145/3410530.3414318Google ScholarGoogle Scholar
  7. Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. ImageNet: a large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition. 248–255. https://doi.org/10.1109/CVPR.2009.5206848Google ScholarGoogle ScholarCross RefCross Ref
  8. Margery Eldridge, Abigail Sellen, and Debra Bekerian. 1992. Memory Problems at Work: Their Range, Frequency and Severity. Rank Xerox EUROPARC. Retrieved from https://europe.naverlabs.com/research/publications/memory-problems-at-work-their-range-frequency-and-severity/Google ScholarGoogle Scholar
  9. Nicholas R. Fyfe and Jon Bannister. 1996. City watching: closed circuit television surveillance in public spaces. Area 28, 1 (1996), 37–46.Google ScholarGoogle Scholar
  10. Julie A. Kientz, Shwetak N. Patel, Arwa Z. Tyebkhan, Brian Gane, Jennifer Wiley, and Gregory D. Abowd. 2006. Where's my stuff? design and evaluation of a mobile system for locating lost items for the visually impaired. In Proceedings of the 8th international ACM SIGACCESS conference on Computers and accessibility (Assets ’06). Association for Computing Machinery, New York, NY, USA, 103–110. https://doi.org/10.1145/1168987.1169006Google ScholarGoogle Scholar
  11. Mizuho Komatsuzaki, Koji Tsukada, Itiro Siio, Pertti Verronen, Mika Luimula, and Sakari Pieskä. 2011. IteMinder: finding items in a room using passive rfid tags and an autonomous robot (poster). In Proceedings of the 13th international conference on Ubiquitous computing - UbiComp ’11. ACM Press, Beijing, China, 599. https://doi.org/10.1145/2030112.2030232Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Sandjar Kozubaev and Carl DiSalvo. 2021. Cracking public space open: design for public librarians. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, 1–14. Retrieved from https://doi.org/10.1145/3411764.3445730Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Xiaotao Liu, Mark D. Corner, and Prashant Shenoy. 2006. Ferret: rfid localization for pervasive multimedia. In Proceedings of the 8th international conference on Ubiquitous Computing (UbiComp’06). Springer-Verlag, Berlin, Heidelberg, 422–440. https://doi.org/10.1007/11853565_25Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Clive Norris and Jade Moran. 2016. Surveillance, Closed Circuit Television and Social Control. Routledge.Google ScholarGoogle Scholar
  15. Ling Pei, Ruizhi Chen, Jingbin Liu, Tomi Tenhunen, Heidi Kuusniemi, and Yuwei Chen. 2010. Inquiry-based bluetooth indoor positioning via rssi probability distributions. In Proceedings of the 2010 Second International Conference on Advances in Satellite and Space Communications (SPACOMM ’10). IEEE Computer Society, USA, 151–156. https://doi.org/10.1109/SPACOMM.2010.18Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Rodney E. Peters, Richard Pak, Gregory D. Abowd, Arthur D. Fisk, and Wendy A. Rogers. 2004. Finding Lost Objects: Informing the Design of Ubiquitous Computing Services for the Home. Georgia Institute of Technology. Retrieved from https://smartech.gatech.edu/handle/1853/51Google ScholarGoogle Scholar
  17. Pixie Technology. 2017. The nation's biggest lost and found survey by pixie. Retrieved January 12, 2022 from https://tinyurl.com/yxrzbsnpGoogle ScholarGoogle Scholar
  18. Sarah Prange, Lukas Mecke, Michael Stadler, Maximilian Balluff, Mohamed Khamis, and Florian Alt. 2019. Securing personal items in public space: stories of attacks and threats. In Proceedings of the 18th International Conference on Mobile and Ubiquitous Multimedia (MUM ’19). Association for Computing Machinery, New York, NY, USA, 1–8. https://doi.org/10.1145/3365610.3365628Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. David Schwarz, Max Schwarz, Jörg Stückler, and Sven Behnke. 2015. Cosero, find my keys! object localization and retrieval using bluetooth low energy tags. In RoboCup 2014: Robot World Cup XVIII (Lecture Notes in Computer Science). Springer International Publishing, Cham, 195–206. https://doi.org/10.1007/978-3-319-18615-3_16Google ScholarGoogle Scholar
  20. Masaya Tanbo, Ryoma Nojiri, Yuusuke Kawakita, and Haruhisa Ichikawa. 2017. Active rfid attached object clustering method with new evaluation criterion for finding lost objects. Mob. Inf. Syst. 2017, (February 2017), e3637814. https://doi.org/10.1155/2017/3637814Google ScholarGoogle ScholarCross RefCross Ref
  21. Paul Wilson, Daniel Prashanth, and Hamid Aghajan. 2007. Utilizing rfid signaling scheme for localization of stationary objects and speed estimation of mobile objects. In 2007 IEEE International Conference on RFID. 94–99. https://doi.org/10.1109/RFID.2007.346155Google ScholarGoogle ScholarCross RefCross Ref
  22. Dan Xie, Tingxin Yan, Deepak Ganesan, and Allen Hanson. 2008. Design and implementation of a dual-camera wireless sensor network for object retrieval. In Proceedings of the 7th international conference on Information processing in sensor networks (IPSN ’08). IEEE Computer Society, USA, 469–480. https://doi.org/10.1109/IPSN.2008.57Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Takuma Yagi, Takumi Nishiyasu, Kunimasa Kawasaki, Moe Matsuki, and Yoichi Sato. 2021. GO-finder: a registration-free wearable system for assisting users in finding lost objects via hand-held object discovery. In 26th International Conference on Intelligent User Interfaces. ACM, College Station TX USA, 139–149. https://doi.org/10.1145/3397481.3450664Google ScholarGoogle ScholarDigital LibraryDigital Library

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  • Published in

    cover image ACM Conferences
    CHI EA '22: Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems
    April 2022
    3066 pages
    ISBN:9781450391566
    DOI:10.1145/3491101

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    Publication History

    • Published: 28 April 2022

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