Multi-Object Tracking in Satellite Videos With Graph-Based Multitask Modeling

doi:10.1109/tgrs.2022.3152250

CORC > 长春光学精密机械与物理研究所 > 中国科学院长春光学精密机械与物理研究所

	Multi-Object Tracking in Satellite Videos With Graph-Based Multitask Modeling
	Q. B. He; X. Sun; Z. Y. Yan; B. B. Li and K. Fu
刊名	Ieee Transactions on Geoscience and Remote Sensing
	2022
卷号	60 页码:13
ISSN号	0196-2892
DOI	10.1109/tgrs.2022.3152250
英文摘要	Recently, satellite video has become an emerging means of earth observation, providing the possibility of tracking moving objects. However, the existing multi-object trackers are commonly designed for natural scenes without considering the characteristics of remotely sensed data. In addition, most trackers are composed of two independent stages of detection and reidentification (ReID), which means that they cannot be mutually promoted. To this end, we propose an end-to-end online framework, which is called TGraM, for multi-object tracking in satellite videos. It models multi-object tracking as a graph information reasoning procedure from the multitask learning perspective. Specifically, a graph-based spatiotemporal reasoning module is presented to mine the potential high-order correlations between video frames. Furthermore, considering the inconsistency of optimization objectives between detection and ReID, a multitask gradient adversarial learning strategy is designed to regularize each task-specific network. In addition, aiming at the data scarcity in this field, a large-scale and high-resolution Jilin-1 satellite video dataset for multi-object tracking (AIR-MOT) is built for the experiments. Compared with state-of-the-art multi-object trackers, TGraM achieves efficient collaborative learning between detection and ReID, improving the tracking accuracy by 1.2 multiple object tracking accuracy. The code and dataset will be available online (https://github.com/HeQibin/TGraM).
URL标识	查看原文
语种	英语
内容类型	期刊论文
源URL	[http://ir.ciomp.ac.cn/handle/181722/66879]
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	Q. B. He,X. Sun,Z. Y. Yan,et al. Multi-Object Tracking in Satellite Videos With Graph-Based Multitask Modeling[J]. Ieee Transactions on Geoscience and Remote Sensing,2022,60:13.
APA	Q. B. He,X. Sun,Z. Y. Yan,&B. B. Li and K. Fu.(2022).Multi-Object Tracking in Satellite Videos With Graph-Based Multitask Modeling.Ieee Transactions on Geoscience and Remote Sensing,60,13.
MLA	Q. B. He,et al."Multi-Object Tracking in Satellite Videos With Graph-Based Multitask Modeling".Ieee Transactions on Geoscience and Remote Sensing 60(2022):13.