Dynamic Subclass-Balancing Contrastive Learning for Long-Tail Pedestrian Trajectory Prediction With Progressive Refinement

Abstract

Pedestrian trajectory prediction is critical for understanding human behavior. The prevailing approaches employ neural networks to predict trajectories from large amounts of trajectory data. However, pedestrian trajectory data exhibits a long-tail distribution, which presents challenges in accurately predicting the future trajectories of tail samples. Previous research utilized contrastive learning and loss reweighting to tackle the long-tail distribution challenge in trajectory prediction. Although this approach enhanced the tail samples’ performance, it reduced the head samples’ performance. In order to address this limitation, we propose a trajectory prediction framework based on dynamic subclass-balancing contrastive learning in this work. Firstly, we obtain general motion patterns by clustering future trajectory data. We use the adaptive motion pattern refinement block to refine the general motion patterns, providing accurate guidance for the model and thus facilitating the recognition of tail motion patterns. Subsequently, we propose dynamic subclass-balancing contrastive learning to address the long-tail distribution issue of trajectory data on the encoder, which includes subclass-balancing clustering and dynamic dual-level contrastive learning. Subclass-balancing clustering is employed on the head trajectory data to achieve subclass balance across the dataset. Afterward, we perform dynamic dual-level contrastive learning for motion features to achieve instance balance and optimize the feature space. Finally, we use enhanced motion features to adjust the predicted trajectories through the trajectory proposal refinement block, achieving progressive refinement. This addresses the long-tail distribution issue of trajectory data on the decoder and improves the model’s generalization capability. Experimental results demonstrate that our method outperforms state-of-the-art long-tail trajectory prediction methods in addressing the long-tail distribution issue, improving the performance on both head and tail samples. The code will be released at https://github.com/YanCCZU/DSBCL-PRM.Note to Practitioners—This work aims to tackle the long-tail distribution issue of pedestrian trajectory prediction while improving the model’s generalization capability. Existing methods mitigate the impact of the long-tail distribution issue on the encoder using contrastive learning. However, their overemphasis on the tail samples through loss reweighting has reduced the head samples’ performance. This work proposes a dynamic subclass-balancing contrastive learning module, which classifies head samples into several subclasses, each with a similar sample number in the tail classes. It performs dynamic dual-level contrastive learning based on class and subclass labels to achieve subclass and instance balance, improving the performance in head and tail samples. We utilize general motion patterns from the training set to guide the prediction of future trajectories. Moreover, we propose a progressive refinement strategy consisting of two refinement blocks to mitigate the impact of the long-tail distribution issue on the decoder and improve the model’s generalization performance. First, we adaptively refine motion patterns based on the difference between observed trajectories and historical motion patterns to provide accurate guidance. Then, we adjust the original predicted trajectories using the enhanced motion features, mitigating the impact of the long-tail distribution issue on the decoder while improving the model’s generalization and adaptability in unknown scenes. Our method’s simplified and effective model design ensures excellent real-time performance. Consequently, it is well-suited for deployment of edge devices in areas such as autonomous driving, intelligent surveillance, and social robotics. The proposed method enables accurate prediction of infrequent future trajectories in various scenarios, thus supporting safer decision-making.