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Multi-Object Tracking With Separation in Deep Space
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AI
DOI：10.1109/TGRS.2024.3522290.png)
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摘要
En 中文
In deep space environment, some objects may split into several small fragments during movement, and these deep space objects often appear as points in satellite images. In this article, we conduct research on multi-object tracking (MOT) for these objects. First, we propose a simulation dataset, ScatterDataset, which simulates the movement and separation of objects in deep space background. By assigning two IDs to a trajectory, we describe the trajectory's relationship before and after separation. Second, we present an end-to-end motion association model, ScatterNet, which encodes the position information of trajectories and detections into motion features. These features are processed through temporal aggregation by a Transformer encoder and spatial aggregation by a graph network; then, we get the association results by calculating the similarity between these features. Finally, we introduce a tracker, ScatterTracker, which is suitable for tracking in scenarios with object separation. Experiments with state-of-the-art tracking methods on ScatterDataset demonstrate that our approach has achieved significant performance improvements in deep space scenarios. The code is available at: https://github.com/wht-bupt/ScatterTrack.
Keyword:
Trajectory
Tracking
Deep-space communications
Videos
Transformers
Predictive models
Kalman filters
Feature extraction
Deep learning
Satellites
Deep space
graph network
multi-object tracking (MOT)
object separation
position encoding
transformer encoder
期刊
IF:
8.6
论文数:
2.2W
被引数:
10.7W
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