TY - JOUR
T1 - Smartphone zombie detection from lidar point cloud for mobile robot safety
AU - Wu, Jiaxu
AU - Tamura, Yusuke
AU - Wang, Yusheng
AU - Woo, Hanwool
AU - Moro, Alessandro
AU - Yamashita, Atsushi
AU - Asama, Hajime
N1 - Funding Information:
Manuscript received September 11, 2019; accepted January 14, 2020. Date of publication January 30, 2020; date of current version February 17, 2020. This letter was recommended for publication by Associate Editor M. C. Yip and Editor P. Rocco upon evaluation of the reviewers’ comments. This work was supported by JSPS KAKENHI under Grant 18K11490. (Corresponding author: Jiaxu Wu.) J. Wu, Y. Tamura, Y. Wang, A. Moro, A. Yamashita, and H. Asama are with the Department of Precision Engineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan (e-mail: wujiaxu@robot. t.u-tokyo.ac.jp; tamura@robot.t.u-tokyo.ac.jp; wang@robot.t.u-tokyo.ac.jp; alessandromoro.italy@gmail.com; yamashita@robot.t.u-tokyo.ac.jp; asama@ robot.t.u-tokyo.ac.jp).
Publisher Copyright:
© 2016 IEEE.
PY - 2020/4
Y1 - 2020/4
N2 - Awareness of surrounding and prediction of dangerous situations is essential for autonomous mobile robots, especially during navigation in a human-populated environment. To cope with safety issues, state-of-The-Art works have focused on pedestrian detection, tracking, and trajectory prediction. However, only a few studies have been conducted on recognizing some specific types of dangerous behaviors exhibited by pedestrians. Here, we propose a tracking enhanced detection method to recognize people using their smartphones while walking, referred to as smartphone zombie. Features used for pedestrian detection usually involve the rotation variance problem, and in this paper, the drawback is handled by employing motion information from multi-object tracking. The proposed solution has been validated through experiments performed on a newly collected dataset. Results showed that our detector can learn a distinct pattern of the appearance of smartphone zombies. Thus, it can successfully detect them outperforming the existed detection method.
AB - Awareness of surrounding and prediction of dangerous situations is essential for autonomous mobile robots, especially during navigation in a human-populated environment. To cope with safety issues, state-of-The-Art works have focused on pedestrian detection, tracking, and trajectory prediction. However, only a few studies have been conducted on recognizing some specific types of dangerous behaviors exhibited by pedestrians. Here, we propose a tracking enhanced detection method to recognize people using their smartphones while walking, referred to as smartphone zombie. Features used for pedestrian detection usually involve the rotation variance problem, and in this paper, the drawback is handled by employing motion information from multi-object tracking. The proposed solution has been validated through experiments performed on a newly collected dataset. Results showed that our detector can learn a distinct pattern of the appearance of smartphone zombies. Thus, it can successfully detect them outperforming the existed detection method.
KW - Robot safety
KW - object detection
KW - range sensing
KW - segmentation and categorization
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U2 - 10.1109/LRA.2020.2970570
DO - 10.1109/LRA.2020.2970570
M3 - Article
AN - SCOPUS:85080965149
VL - 5
SP - 2256
EP - 2263
JO - IEEE Robotics and Automation Letters
JF - IEEE Robotics and Automation Letters
SN - 2377-3766
IS - 2
M1 - 8976312
ER -