TY - GEN
T1 - FIOU Tracker: An Improved Algorithm of IOU Tracker in Video with a Lot of Background Inferences
AU - Chen, Zhihua
AU - Qiu, Guhao
AU - Zhang, Han
AU - Sheng, Bin
AU - Li, Ping
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/10
Y1 - 2020/10
N2 - Multiple object tracking(MOT) is a fundamental problem in video analysis application. Associating unreliable detection in a complex environment is a challenging task. The accuracy of multiple object tracking algorithms is dependent on the accuracy of the first stage object detection algorithm. In this paper, we propose an improved algorithm of IOU Tracker–FIOU Tracker. Our proposal algorithm can overcome the shortcoming of IOU Tracker with a small amount of computing cost that heavily relies on the precision and recall of object detection accuracy. The algorithm we propose is based on the assumption that the motion of background inference is not obvious. We use the average light flux value of the track and the change rate of the light flux value of the center point of the adjacent object as the conditions to determine whether the trajectory is to be retained. The tracking accuracy is higher than the primary IOU Tracker and another well-known variant VIOU Tracker. Our proposal method can also significantly reduce the ID switch value and fragmentation value which are both important metrics in MOT task.
AB - Multiple object tracking(MOT) is a fundamental problem in video analysis application. Associating unreliable detection in a complex environment is a challenging task. The accuracy of multiple object tracking algorithms is dependent on the accuracy of the first stage object detection algorithm. In this paper, we propose an improved algorithm of IOU Tracker–FIOU Tracker. Our proposal algorithm can overcome the shortcoming of IOU Tracker with a small amount of computing cost that heavily relies on the precision and recall of object detection accuracy. The algorithm we propose is based on the assumption that the motion of background inference is not obvious. We use the average light flux value of the track and the change rate of the light flux value of the center point of the adjacent object as the conditions to determine whether the trajectory is to be retained. The tracking accuracy is higher than the primary IOU Tracker and another well-known variant VIOU Tracker. Our proposal method can also significantly reduce the ID switch value and fragmentation value which are both important metrics in MOT task.
KW - Drone video
KW - FIOU Tracker
KW - Multiple object tracking
UR - http://www.scopus.com/inward/record.url?scp=85096594104&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-61864-3_13
DO - 10.1007/978-3-030-61864-3_13
M3 - Conference article published in proceeding or book
AN - SCOPUS:85096594104
SN - 9783030618636
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 145
EP - 156
BT - Advances in Computer Graphics - 37th Computer Graphics International Conference, CGI 2020, Proceedings
A2 - Magnenat-Thalmann, Nadia
A2 - Stephanidis, Constantine
A2 - Papagiannakis, George
A2 - Wu, Enhua
A2 - Thalmann, Daniel
A2 - Sheng, Bin
A2 - Kim, Jinman
A2 - Gavrilova, Marina
PB - Springer Science and Business Media Deutschland GmbH
T2 - 37th Computer Graphics International Conference, CGI 2020
Y2 - 20 October 2020 through 23 October 2020
ER -