Multi-Target Tracking for Satellite Videos Guided by Spatial-Temporal Proximity and Topological Relationships

The features of moving targets in satellite videos are sparse and similar, resulting in two major challenges for multi-target tracking: detection losses and association errors. The rich spatial-temporal proximity and topological relationships among targets in satellite videos can indicate the feature enhancement of small targets and generate discriminative individual descriptions of targets, which helps to improve the accuracy of multi-target tracking. In this article, we propose a novel Multi-target Tracking method for satellite videos guided by Spatial-Temporal Proximity and Topological Relationships (MTT-STPTR). Specifically, a spatial-temporal relationship sparse attention (STRSA) module is constructed in the feature extraction stage to accurately enhance the feature expression of small targets by capturing the cross-frame semantics relevant to the target areas. In addition, a joint feature matching (JFM) module is designed in the interframe association stage, which constructs a novel similarity measurement method of star-shaped topological structures and uses it to measure the similarity of multidimensional features of target individuals, thereby alleviating association errors caused by dense individuals with similar features. Moreover, a novel multiple granularity spatial-temporal contrastive learning (MGSTCL) module is designed to promote a balanced optimization of detection and association tasks for multicategory targets in satellite videos. Experiments conducted on two public datasets, VISO and AIR-MOT, demonstrate that MTT-STPTR outperforms existing state-of-the-art multi-target tracking methods in terms of the multiobject-tracking accuracy (MOTA) and identification F1-score (IDF1), indicating its effectiveness.