  Tracking in gigapixel scenarios holds numerous potential applications in
video surveillance and pedestrian analysis. Existing algorithms attempt to
perform tracking in crowded scenes by utilizing multiple cameras or group
relationships. However, their performance significantly degrades when
confronted with complex interaction and occlusion inherent in gigapixel images.
In this paper, we introduce DynamicTrack, a dynamic tracking framework designed
to address gigapixel tracking challenges in crowded scenes. In particular, we
propose a dynamic detector that utilizes contrastive learning to jointly detect
the head and body of pedestrians. Building upon this, we design a dynamic
association algorithm that effectively utilizes head and body information for
matching purposes. Extensive experiments show that our tracker achieves
state-of-the-art performance on widely used tracking benchmarks specifically
designed for gigapixel crowded scenes.
