GigaTraj: Predicting Long-term Trajectories of Hundreds of Pedestrians in Gigapixel Complex Scenes

Haozhe Lin, Chunyu Wei, Li He, Yuchen Guo, Yunqi Zhao, Shanglong Li, Lu Fang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 19331-19340

Pedestrian trajectory prediction is a well-established task with significant recent advancements. However existing datasets are unable to fulfill the demand for studying minute-level long-term trajectory prediction mainly due to the lack of high-resolution trajectory observation in the wide field of view (FoV). To bridge this gap we introduce a novel dataset named GigaTraj featuring videos capturing a wide FoV with ~ 4 x10^4 m^2 and high-resolution imagery at the gigapixel level. Furthermore GigaTraj includes comprehensive annotations such as bounding boxes identity associations world coordinates group/interaction relationships and scene semantics. Leveraging these multimodal annotations we evaluate and validate the state-of-the-art approaches for minute-level long-term trajectory prediction in large-scale scenes. Extensive experiments and analyses have revealed that long-term prediction for pedestrian trajectories presents numerous challenges indicating a vital new direction for trajectory research. The dataset is available at www.gigavision.ai.