MP<sup>2</sup>Net: Mask Propagation and Motion Prediction Network for Multiobject Tracking in Satellite Videos
Manqi Zhao, Shengyang Li, Han Wang, Jian Yang, Yuhan Sun, Yanfeng Gu
Abstract
Mainstream multi-object tracking (MOT) algorithms employ global object detection and association methods. However, when dealing with scenarios involving crowded tiny objects in satellite videos, existing global trackers often yield numerous missed detections and unstable trajectories. To address this issue, we propose a novel joint-detection-and-tracking framework, MP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Net, which integrates local detection enhancements for tiny targets and bridges the gap between detection and association. Specifically, our approach incorporates a mask propagation network that enhances feature representation for tiny targets by matching frame-by-frame to capture local details. Additionally, we utilize an implicit and explicit motion prediction strategy that merges tracking information into detection at both feature and instance levels, thereby improving tracking robustness. Experimental results on two large-scale datasets demonstrate the effectiveness and robustness of MP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Net, achieving state-of-the-art performance on typical moving objects in satellite videos, such as 66.7% MOTA and 75.9% IDF1 on the SatVideoDT challenge dataset. The code will be available at https://github.com/DonDominic/MP2Net.