TY - GEN
T1 - Autonomous tracking and counting of footsteps by mobile phone cameras
AU - Ozcan, Koray
AU - Mahabalagiri, Anvith
AU - Velipasalar, Senem
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/2/26
Y1 - 2016/2/26
N2 - In this paper, we present an autonomous method to track and count footsteps by using the camera data from mobile phones or tablets. Many step counters, relying on accelerometer data, are widely available. However, accelerometer-based algorithms are prone to overcounting. In our proposed method, feature points are detected first. Then, in order to increase robustness and accuracy especially in the case of highly-textured ground and floor surfaces, Kalman filter based tracking is performed. The proposed method is compared with existing accelerometer- based step counters. Experiments are performed with multiple subjects carrying five mobile devices simultaneously, including smart phones and watches, at different locations on their body. A Samsung Galaxy S4 smartphone is used to capture the videos. The results show that the proposed camera-based footstep counting has the lowest average error rate for different users, and is more reliable compared to accelerometer-based counters. The average error rate for the proposed method is 2.68%, and the standard deviation of the error is 2.39%.
AB - In this paper, we present an autonomous method to track and count footsteps by using the camera data from mobile phones or tablets. Many step counters, relying on accelerometer data, are widely available. However, accelerometer-based algorithms are prone to overcounting. In our proposed method, feature points are detected first. Then, in order to increase robustness and accuracy especially in the case of highly-textured ground and floor surfaces, Kalman filter based tracking is performed. The proposed method is compared with existing accelerometer- based step counters. Experiments are performed with multiple subjects carrying five mobile devices simultaneously, including smart phones and watches, at different locations on their body. A Samsung Galaxy S4 smartphone is used to capture the videos. The results show that the proposed camera-based footstep counting has the lowest average error rate for different users, and is more reliable compared to accelerometer-based counters. The average error rate for the proposed method is 2.68%, and the standard deviation of the error is 2.39%.
UR - http://www.scopus.com/inward/record.url?scp=84969849477&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84969849477&partnerID=8YFLogxK
U2 - 10.1109/ACSSC.2015.7421374
DO - 10.1109/ACSSC.2015.7421374
M3 - Conference contribution
AN - SCOPUS:84969849477
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 1408
EP - 1412
BT - Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
A2 - Matthews, Michael B.
PB - IEEE Computer Society
T2 - 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
Y2 - 8 November 2015 through 11 November 2015
ER -