TY - GEN
T1 - EagleSense: tracking people and devices in interactive spaces using real-time top-view depth-sensing
AU - Wu, Chi-Jui
AU - Houben, Steven
AU - Marquardt, Nicolai
N1 - ©The Authors. 2017. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in CHI '17 Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems http://dx.doi.org/10.1145/3025453.3025562
PY - 2017/5/6
Y1 - 2017/5/6
N2 - Real-time tracking of people's location, orientation and activities is increasingly important for designing novel ubiquitous computing applications. Top-view camera-based tracking avoids occlusion when tracking people while collaborating, but often requires complex tracking systems and advanced computer vision algorithms. To facilitate the prototyping of ubiquitous computing applications for interactive spaces, we developed EagleSense, a real-time human posture and activity recognition system with a single top-view depth sensing camera. We contribute our novel algorithm and processing pipeline, including details for calculating silhouetteextremities features and applying gradient tree boosting classifiers for activity recognition optimised for top-view depth sensing. EagleSense provides easy access to the real-time tracking data and includes tools for facilitating the integration into custom applications. We report the results of a technical evaluation with 12 participants and demonstrate the capabilities of EagleSense with application case studies.
AB - Real-time tracking of people's location, orientation and activities is increasingly important for designing novel ubiquitous computing applications. Top-view camera-based tracking avoids occlusion when tracking people while collaborating, but often requires complex tracking systems and advanced computer vision algorithms. To facilitate the prototyping of ubiquitous computing applications for interactive spaces, we developed EagleSense, a real-time human posture and activity recognition system with a single top-view depth sensing camera. We contribute our novel algorithm and processing pipeline, including details for calculating silhouetteextremities features and applying gradient tree boosting classifiers for activity recognition optimised for top-view depth sensing. EagleSense provides easy access to the real-time tracking data and includes tools for facilitating the integration into custom applications. We report the results of a technical evaluation with 12 participants and demonstrate the capabilities of EagleSense with application case studies.
U2 - 10.1145/3025453.3025562
DO - 10.1145/3025453.3025562
M3 - Conference contribution
SN - 9781450346559
SP - 3929
EP - 3942
BT - CHI '17 Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems
PB - ACM/IEEE
ER -