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

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).

Automated summary

This paper proposes an end-to-end online framework called TGraM for multi-object tracking in satellite videos. It also builds a large-scale satellite video dataset for experiments. Compared with existing multi-object trackers, TGraM achieves efficient collaborative learning between detection and reidentification, improving tracking accuracy.