DynamicTrack: Advancing Gigapixel Tracking in Crowded Scenes
This addresses tracking challenges in crowded surveillance scenarios, but appears incremental as it builds on existing detection and association techniques.
The paper tackles tracking in gigapixel crowded scenes, where existing methods degrade due to complex interactions and occlusions, and introduces DynamicTrack, a framework that achieves state-of-the-art performance on benchmarks.
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.