TY - GEN
T1 - Combining passive visual cameras and active IMU sensors to track cooperative people
AU - Jiang, Wenchao
AU - Yin, Zhaozheng
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/9/14
Y1 - 2015/9/14
N2 - We attack the problem of persistently tracking cooperative people such as children, the elderly or patients by combining passive tracking and active tracking techniques. Passive tracking uses visual signals from surveillance cameras, but vision based people tracking becomes a hard problem in challenging scenarios such as long-term/heavy occlusion, people changing their movement patterns during occlusion, or people temporarily moving out of the visual field. Active tracking uses sensor signals from Inertial Measurement Unit (IMU) carried by targets themselves. IMU-based tracking is independent of visual signals, so it keeps working when people are visually occluded and offers clues where the target could be, helping the visual tracking to reidentify the target. Meanwhile, when visual signals on people are available, visual tracking can calibrate IMU-based tracking to avoid sensor drift. The experimental results show that the IMU and visual tracking are complementary to each other and their combination performs robustly on tracking cooperative people in many challenging scenarios.
AB - We attack the problem of persistently tracking cooperative people such as children, the elderly or patients by combining passive tracking and active tracking techniques. Passive tracking uses visual signals from surveillance cameras, but vision based people tracking becomes a hard problem in challenging scenarios such as long-term/heavy occlusion, people changing their movement patterns during occlusion, or people temporarily moving out of the visual field. Active tracking uses sensor signals from Inertial Measurement Unit (IMU) carried by targets themselves. IMU-based tracking is independent of visual signals, so it keeps working when people are visually occluded and offers clues where the target could be, helping the visual tracking to reidentify the target. Meanwhile, when visual signals on people are available, visual tracking can calibrate IMU-based tracking to avoid sensor drift. The experimental results show that the IMU and visual tracking are complementary to each other and their combination performs robustly on tracking cooperative people in many challenging scenarios.
UR - https://www.scopus.com/pages/publications/84960509965
M3 - Conference contribution
AN - SCOPUS:84960509965
T3 - 2015 18th International Conference on Information Fusion, Fusion 2015
SP - 1338
EP - 1345
BT - 2015 18th International Conference on Information Fusion, Fusion 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th International Conference on Information Fusion, Fusion 2015
Y2 - 6 July 2015 through 9 July 2015
ER -