Robust Human Trajectory Prediction via Self-Supervised Skeleton Representation Learning
Human trajectory prediction plays a crucial role in applications such as autonomous navigation and video surveillance. While recent works have explored the integration of human skeleton sequences to complement trajectory information, skeleton data in real-world environments often suffer from missing joints caused by occlusions. These disturbances significantly degrade prediction accuracy, indicating the need for more robust skeleton representations. We propose a robust trajectory prediction method that incorporates a self-supervised skeleton representation model pretrained with masked autoencoding. Experimental results in occlusion-prone scenarios show that our method improves robustness to missing skeletal data without sacrificing prediction accuracy, and consistently outperforms baseline models in clean-to-moderate missingness regimes.
Code (0)
등록된 구현이 없습니다.
Tasks
Representation LearningTrajectory PredictionSimilar Papers 제목 키워드 기반
Hierarchically Self-Supervised Transformer for Human Skeleton Representation Learning
Despite the success of fully-supervised human skeleton sequence modeling, utilizing self-supervised pre-training for skeleton sequence representation learning has been an active field because acquiring task-specific skel…
Action DetectionAction RecognitionContrastive Learningmotion prediction+1SM-SGE: A Self-Supervised Multi-Scale Skeleton Graph Encoding Framework for Person Re-Identification
Person re-identification via 3D skeletons is an emerging topic with great potential in security-critical applications. Existing methods typically learn body and motion features from the body-joint trajectory, whereas the…
Person Re-IdentificationRelation NetworkMS$^2$L: Multi-Task Self-Supervised Learning for Skeleton Based Action Recognition
In this paper, we address self-supervised representation learning from human skeletons for action recognition. Previous methods, which usually learn feature presentations from a single reconstruction task, may come acros…
Action RecognitionContrastive Learningmotion predictionRepresentation Learning+2Contrastive Self-Supervised Learning for Skeleton Representations
Human skeleton point clouds are commonly used to automatically classify and predict the behaviour of others. In this paper, we use a contrastive self-supervised learning method, SimCLR, to learn representations that capt…
Graph Neural Networkmotion predictionSelf-Supervised LearningDeep self-supervised learning with visualisation for automatic gesture recognition
Gesture is an important mean of non-verbal communication, with visual modality allows human to convey information during interaction, facilitating peoples and human-machine interactions. However, it is considered difficu…
Gesture RecognitionSelf-Supervised Learning