EagleSense : tracking people and devices in interactive spaces using real-time top-view depth-sensing
- Submitting institution
-
The University of Lancaster
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 156626055
- Type
- E - Conference contribution
- DOI
-
10.1145/3025453.3025562
- Title of conference / published proceedings
- CHI '17 Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems
- First page
- 3929
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- May
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
E - Interactive Systems
- Citation count
- 13
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work presents a novel tracking algorithm for ‘proxemic’ or spatial interaction. The toolkit ‘Eaglesense’ provides real-time activity recognition algorithm using an off-the-shelve ceiling-mounted depth camera. This project democratises the access and usage of real-time activity recognition and is available as open source software. EagleSense is published in the ACM CHI proceedings - the top publication venue for HCI research - and has been presented in public talks to industry (e.g., Microsoft Research), invited talks at academic institutions (e.g., Aarhus and UCL) and in various workshops and seminars.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
