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Multi-object tracking by detecting small objects in satellite video

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Spectral Imaging Technology, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：cuihaowen20@mails.ucas.ac.cn
  • Introduction：崔浩文，研究方向为计算机视觉。E-mail：cuihaowen20@mails.ucas.ac.cn
CUI Haowen12,  
  • Affiliation：

    Key Laboratory of Spectral Imaging Technology, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China

    University of Chinese Academy of Sciences, Beijing 100049, China

XU Chujie12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Key Laboratory of Spectral Imaging Technology, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China

  • Email：zhengxiangtao@opt.ac.cn
  • Introduction：郑向涛，研究方向为计算机视觉。E-mail：zhengxiangtao@opt.ac.cn
ZHENG Xiangtao1*,  
  • Affiliation：

    Key Laboratory of Spectral Imaging Technology, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China

LU Xiaoqiang1

реферат

Multi-object tracking determines the position of an object and estimates the trajectory of objects in remote sensing satellite videos. This method has attracted considerable interest, and its application to security monitoring, motion analysis, and intelligent transportation has been explored. Compared with surveillance videos, remote sensing satellite videos contain smaller objects and a larger background, and thus the foreground object is difficult to detect. In addition, remote sensing satellite videos are extremely large, requiring massive computation and storage. Multi-object tracking in remote sensing satellite videos have high real-time requirements. Based on the mentioned problems, a multi-object tracking method for remote sensing satellite videos is proposed in this paper, which adopts tracking-by-detection paradigm. First, the backbone added a transformer that capture the global context information in the detection stage, enabling the detector to distinguish between objects and background. Then, an attention mechanism was used to enhance objects’ features, enabling the proposed method to focus on the region of objects. Finally, an extra prediction branch was added to the network to generat a high-resolution feature map, which retained the details of small objects and was beneficial to small-object detection. Owing to the small objects and occlusion in remote sensing satellite videos, the confidence of hard positive samples was quite low. In the data association stage, an association strategy was adopted, which considered high and low confidence detection simultaneously and associated detected small objects with existing trajectories. To verify the effectiveness of the proposed method, ablation and comparison experiments were carried out on the remote sensing satellite videos dataset. The proposed method achieved 63.1% MOTA and 78.0% IDF1. The proposed method showed optimal performance, which reflected its suitability for multi-object tracking in remote sensing satellite videos. The proposed method ranked second in the multi-object tracking challenge of the 2021 Gaofen Challenge. The proposed method was dedicated to solving the difficulty of small-object tracking in remote sensing satellite videos, and some helpful methods for small-object tracking were used. Experimental results showed that the proposed method can improve the performance of multi-object tracking in remote sensing satellite videos.

ключеви́че слова́

remote sensing;multi-object tracking;small object detection;attention;neural network;trajectory tracking

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