  Tracking many vehicles in wide coverage aerial imagery is crucial for
understanding events in a large field of view. Most approaches aim to associate
detections from frame differencing into tracks. However, slow or stopped
vehicles result in long-term missing detections and further cause tracking
discontinuities. Relying merely on appearance clue to recover missing
detections is difficult as targets are extremely small and in grayscale. In
this paper, we address the limitations of detection association methods by
coupling it with a local context tracker (LCT), which does not rely on motion
detections. On one hand, our LCT learns neighboring spatial relation and tracks
each target in consecutive frames using graph optimization. It takes the
advantage of context constraints to avoid drifting to nearby targets. We
generate hypotheses from sparse and dense flow efficiently to keep solutions
tractable. On the other hand, we use detection association strategy to extract
short tracks in batch processing. We explicitly handle merged detections by
generating additional hypotheses from them. Our evaluation on wide area aerial
imagery sequences shows significant improvement over state-of-the-art methods.
