文献类型：期刊文献

英文题名：FIOU Tracker: An Improved Algorithm of IOU Tracker in Video with a Lot of Background Inferences

作者：Chen, Zhihua[1]; Qiu, Guhao[1]; Zhang, Han[2]; Sheng, Bin[3]; Li, Ping[4]

机构：[1] Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China; [2] Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China; [3] Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; [4] Faculty of Information Technology, Macau University of Science and Technology, Macau, 999078, China

年份：2020

卷号：12221 LNCS

起止页码：145

外文期刊名：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

收录：EI(收录号：20204809544916)

语种：英文

外文关键词：Computer vision - Inference engines - Object recognition - Aircraft detection - Object detection

摘要：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. ? 2020, Springer Nature Switzerland AG.