Abstract
People tracking is one of the fundamental goals of human behavior recognition. Development of cameras, tracking algorithms and effective computations make it appropriate. But, when the question is privacy and secrecy, cameras have a great obligation on it. Our fundamental goal of this research is to replace video camera with a device (2D LiDAR) that significantly preserve the privacy of the user, solve the issue of narrow field of view and make the system functional simultaneously. We consider individual movements of every moving objects on the plane and figure out the objects as a person based on ankle orientation and movements. Our approach calculates the number of frames of every moving object and finally create a video based on those frames.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Chen, L., Ai, H., Zhuang, Z., Shang, C.: Multiple people tracking with deeply learned candidate selection and person re-identification. In: Proceedings of Multimedia and Expo (ICME) (2018)
Rashed, M.G., Suzuki, R., Yonezawa, T., Lam, A., Kobayashi, Y., Kuno, Y.: Robustly tracking people with LIDARs in a crowded museum for behavioral analysis. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. E100 A, 2458 (2017)
Oyama, T., Yoshida, E., Kobayashi, Y., Kuno, Y.: Tracking visitors with sensor poles for robot’s museum guide tour. In: Proceedings of Human System Interactions (HSI), Sopot, pp. 645–650 (2013)
Oyama, T., Yoshida, E., Kobayashi, Y., Kuno, Y.: Tracking a Robot and Visitors in a Museum Using Sensor Poles. Proc. of Frontiers of Computer Vision, pp. 36–41, (2013)
Misu, K., Miura, J.: Specific person detection and tracking by a mobile robot using 3D LIDAR and ESPAR antenna. In: Proceedings of Intelligent Autonomous Systems (IAS) (2014)
Zhi, Y., Sun, L., Duckctr, T., Bellotto, N.: Multisensor online transfer learning for 3D LiDAR-based human detection with a mobile robot. In: Proceedings of Intelligent Robots and Systems (IROS), pp. 7635–7640 (2018)
Koide, K., Miura, J.: Person identification based on the matching of foot strike timings obtained by LRFs and a smartphone. In: Proceedings of Intelligent Robots and Systems (IROS), pp: 4187–4192 (2016)
Álvarez-Aparicio, C., Guerrero-Higueras, Á.M., Rodríguez-Lera, F.J., Clavero, J.G., Rico, F.M., Matellán, V.: People detection and tracking using LIDAR sensors. Robotics 8, 75 (2019)
Dimitrievski, M., Veelaert, P., Philips, W.: Behavioral pedestrian tracking using a camera and LiDAR sensors on a moving vehicle. Sensors 19, 391 (2019)
Sualeh, M., Kim, G.-W.: Dynamic Multi-LiDAR Based Multiple Object Detection and Tracking. Sensors. 19, 6, 1474 (2019)
Gálai, B., Benedek, C.: Feature selection for Lidar-based gait recognition. In: Proceedings of Computational Intelligence for Multimedia Understanding (IWCIM), Prague, pp. 1–5 (2015)
Benedek, C., Nagy, B., Gálai, B., Jankó, Z.: Lidar-based gait analysis in people tracking and 4D visualization. In: Proceedings of European Signal Processing Conference (EUSIPCO), Nice, pp. 1138–1142 (2015)
Li, Q., Chen, S., Wang, C., Li, X., Wen, C., Cheng, M., Li, J.: LO-Net: deep real-time lidar odometry. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), pp. 8473–8482 (2019)
Qiu, I., Cui, Z., Zhang, Y., Zhang, X., Liu, S., Zeng, B., Pollefeys, M.: DeepLiDAR: deep surface normal guided depth prediction for outdoor scene from sparse liDAR data and single-color image. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), pp. 3313–3322 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Hasan, M., Hanawa, J., Goto, R., Fukuda, H., Kuno, Y., Kobayashi, Y. (2021). Tracking People Using Ankle-Level 2D LiDAR for Gait Analysis. In: Ahram, T. (eds) Advances in Artificial Intelligence, Software and Systems Engineering. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1213. Springer, Cham. https://doi.org/10.1007/978-3-030-51328-3_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-51328-3_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-51327-6
Online ISBN: 978-3-030-51328-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)