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Tracking many vehicles in wide coverage aerial imagery is crucial forunderstanding events in a large field of view. Most approaches aim to associatedetections from frame differencing into tracks. However, slow or stoppedvehicles result in long-term missing detections and further cause trackingdiscontinuities. Relying merely on appearance clue to recover missingdetections is difficult as targets are extremely small and in grayscale. Inthis paper, we address the limitations of detection association methods bycoupling it with a local context tracker (LCT), which does not rely on motiondetections. On one hand, our LCT learns neighboring spatial relation and trackseach target in consecutive frames using graph optimization. It takes theadvantage of context constraints to avoid drifting to nearby targets. Wegenerate hypotheses from sparse and dense flow efficiently to keep solutionstractable. On the other hand, we use detection association strategy to extractshort tracks in batch processing. We explicitly handle merged detections bygenerating additional hypotheses from them. Our evaluation on wide area aerialimagery sequences shows significant improvement over state-of-the-art methods.

Exploring Local Context for Multi-target Tracking in Wide Area Aerial Surveillance