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In order to obtain the satellite's in-orbit attitude information, it is necessary to track the satellite components in satellite video sequences. To solve the problem of low illumination and target occlusion in space environment, we propose an efficient satellite component tracking technique based on Rethinking Space-Time Networks with Improved Memory Coverage (STCN). We classify the pixels in the query frame by feature matching network that establishes the corresponding relationship between the frames. Unlike STCN, we reduce the contribution of background region in feature matching and enhance the robustness of the model in low illumination environment, thus improving the segmentation results. For lost targets due to the overturning and occlusion of satellite components, a position information encoder module is designed to further raise the tracking performance of the model. In addition, we present a local matching module to upgrade the existing feature matching methods. Experiments demonstrate that compared to STCN, our method heightens the tracking performance (J&F) by 10.1% and can achieve multiobject recognition at 15+ FPS.

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