DynamicTrack: Advancing Gigapixel Tracking in Crowded Scenes

TL;DR

DynamicTrack is a novel framework that improves gigapixel crowd tracking by using a contrastive learning-based detector and a dynamic association algorithm, achieving state-of-the-art results in complex scenes.

Contribution

We introduce a dynamic detector with contrastive learning and a dynamic association method tailored for gigapixel crowded scene tracking, addressing occlusion and interaction challenges.

Findings

Achieves state-of-the-art performance on gigapixel crowded scene benchmarks.

Effectively handles occlusion and complex interactions in crowded scenes.

Outperforms existing algorithms in accuracy and robustness.

Abstract

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.