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

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 ×10
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m
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and high-resolution imagery at the gigapixel level. Furthermore, GigaTraj in-cludes 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.