Abstract
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%.
Original language | English (US) |
---|---|
Title of host publication | Conference Record - Asilomar Conference on Signals, Systems and Computers |
Publisher | IEEE Computer Society |
Pages | 1408-1412 |
Number of pages | 5 |
Volume | 2016-February |
ISBN (Print) | 9781467385763 |
DOIs | |
State | Published - Feb 26 2016 |
Event | 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States Duration: Nov 8 2015 → Nov 11 2015 |
Other
Other | 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 |
---|---|
Country/Territory | United States |
City | Pacific Grove |
Period | 11/8/15 → 11/11/15 |
ASJC Scopus subject areas
- Computer Networks and Communications
- Signal Processing