Abstract:  With the widespread use of area array detectors，multitarget tracking for video satellites has become of great significance.However，for multi-target tracking methods based on graph structure，in the construction of graphs，most of them extract clues from adjacent frames，ignoring the previous frame clues.In response to the above problems，an end-to-end graph network framework was proposed to construct the nodes，edges and global variables of the graph，using various clues such as motion features，appearance features，and topology information extracted from multiple frames.A key principle to realize this unified framework is to design compatible feature representations and graph network update mechanisms for different clues and different sources（trajectories and detection targets）.The framework operated in a feed-forward fashion and trained on line.Being evaluated on the public datasets VISO，MOT16，MOT17 benchmarks，the multi-target tracking accuracy of 99.8%，48.8% and 51.8% was achieved respectively，which was better than other related multi-target tracking algorithms.And the ablation experiments were used to verify the improvement that each clue tracks multiple targets.The effectiveness of performance improvement will have a wide range of application scenarios in many fields such as smart transportation，smart cities，and military warfare in the future.

Key words: multi-target tracking, video satellite, graph structure, spatiotemporal information, motion feature, appearance feature