Learning spatio-temporal attention for multi-object tracking and re-identification in wide area motion imagery

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Multi-object tracking in wide-area motion imagery (WAMI) is facilitating great interest in the field of image processing that leads to numerous real-world applications. Among them, aircraft and unmanned aerial vehicles (UAV) with real-time robust visual trackers for long-term aerial maneuvering are currently attracting attention and have remarkably broadened the scope of applications of object tracking. In this paper, we present a novel attention-based feature fusion strategy, which effectively combines the template and searching region features. Our results demonstrate the efficacy of the proposed system on CLIF and UNICORN datasets.