Sports-Traj: A Unified Trajectory Generation Model for Multi-Agent Movement in Sports

• URL: http://arxiv.org/abs/2405.17680v2
• Date: Wed, 26 Feb 2025 23:35:18 GMT
• Title: Sports-Traj: A Unified Trajectory Generation Model for Multi-Agent Movement in Sports
• Authors: Yi Xu, Yun Fu,
• Abstract summary: We propose a Unified Trajectory Generation model, UniTraj, that processes arbitrary trajectories as masked inputs.<n>Specifically, we introduce a Ghost Spatial Masking (GSM) module, embedded within a Transformer encoder, for spatial feature extraction.<n>We benchmark three practical sports datasets, Basketball-U, Football-U, and Soccer-U, for evaluation.
• Score: 53.637837706712794
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Understanding multi-agent movement is critical across various fields. The conventional approaches typically focus on separate tasks such as trajectory prediction, imputation, or spatial-temporal recovery. Considering the unique formulation and constraint of each task, most existing methods are tailored for only one, limiting the ability to handle multiple tasks simultaneously, which is a common requirement in real-world scenarios. Another limitation is that widely used public datasets mainly focus on pedestrian movements with casual, loosely connected patterns, where interactions between individuals are not always present, especially at a long distance, making them less representative of more structured environments. To overcome these limitations, we propose a Unified Trajectory Generation model, UniTraj, that processes arbitrary trajectories as masked inputs, adaptable to diverse scenarios in the domain of sports games. Specifically, we introduce a Ghost Spatial Masking (GSM) module, embedded within a Transformer encoder, for spatial feature extraction. We further extend recent State Space Models (SSMs), known as the Mamba model, into a Bidirectional Temporal Mamba (BTM) to better capture temporal dependencies. Additionally, we incorporate a Bidirectional Temporal Scaled (BTS) module to thoroughly scan trajectories while preserving temporal missing relationships. Furthermore, we curate and benchmark three practical sports datasets, Basketball-U, Football-U, and Soccer-U, for evaluation. Extensive experiments demonstrate the superior performance of our model. We hope that our work can advance the understanding of human movement in real-world applications, particularly in sports. Our datasets, code, and model weights are available here https://github.com/colorfulfuture/UniTraj-pytorch.

Related papers

• UniSTD: Towards Unified Spatio-Temporal Learning across Diverse Disciplines [64.84631333071728]
  We introduce bfUnistage, a unified Transformer-based framework fortemporal modeling. Our work demonstrates that a task-specific vision-text can build a generalizable model fortemporal learning. We also introduce a temporal module to incorporate temporal dynamics explicitly.
  arXiv  Detail & Related papers  (2025-03-26T17:33:23Z)
• TacticExpert: Spatial-Temporal Graph Language Model for Basketball Tactics [0.0]
  Basketball tactic modeling needs to efficiently extract complex spatial-temporal dependencies from historical data. Existing state-of-the-art (SOTA) models, primarily based on graph neural networks (GNNs), encounter difficulties in capturing long-term, long-distance, and fine-grained interactions.
  arXiv  Detail & Related papers  (2025-03-13T08:27:24Z)
• Multi-Transmotion: Pre-trained Model for Human Motion Prediction [68.87010221355223]
  Multi-Transmotion is an innovative transformer-based model designed for cross-modality pre-training. Our methodology demonstrates competitive performance across various datasets on several downstream tasks.
  arXiv  Detail & Related papers  (2024-11-04T23:15:21Z)
• TranSPORTmer: A Holistic Approach to Trajectory Understanding in Multi-Agent Sports [28.32714256545306]
  TranSPORTmer is a unified transformer-based framework capable of addressing all these tasks. It effectively captures temporal dynamics and social interactions in an equivariant manner. It outperforms state-of-the-art task-specific models in player forecasting, player forecasting-imputation, ball inference, and ball imputation.
  arXiv  Detail & Related papers  (2024-10-23T11:35:44Z)
• TIMBA: Time series Imputation with Bi-directional Mamba Blocks and Diffusion models [0.0]
  We propose replacing time-oriented Transformers with State-Space Models (SSM) We develop a model that integrates SSM, Graph Neural Networks, and node-oriented Transformers to achieve enhanced representations.
  arXiv  Detail & Related papers  (2024-10-08T11:10:06Z)
• MambaTrack: A Simple Baseline for Multiple Object Tracking with State Space Model [18.607106274732885]
  We introduce a Mamba-based motion model named Mamba moTion Predictor (MTP) MTP takes the spatial-temporal location dynamics of objects as input, captures the motion pattern using a bi-Mamba encoding layer, and predicts the next motion. Our proposed tracker, MambaTrack, demonstrates advanced performance on benchmarks such as Dancetrack and SportsMOT.
  arXiv  Detail & Related papers  (2024-08-17T11:58:47Z)
• DeTra: A Unified Model for Object Detection and Trajectory Forecasting [68.85128937305697]
  Our approach formulates the union of the two tasks as a trajectory refinement problem. To tackle this unified task, we design a refinement transformer that infers the presence, pose, and multi-modal future behaviors of objects. In our experiments, we observe that ourmodel outperforms the state-of-the-art on Argoverse 2 Sensor and Open dataset.
  arXiv  Detail & Related papers  (2024-06-06T18:12:04Z)
• Modeling Continuous Motion for 3D Point Cloud Object Tracking [54.48716096286417]
  This paper presents a novel approach that views each tracklet as a continuous stream. At each timestamp, only the current frame is fed into the network to interact with multi-frame historical features stored in a memory bank. To enhance the utilization of multi-frame features for robust tracking, a contrastive sequence enhancement strategy is proposed.
  arXiv  Detail & Related papers  (2023-03-14T02:58:27Z)
• Snipper: A Spatiotemporal Transformer for Simultaneous Multi-Person 3D Pose Estimation Tracking and Forecasting on a Video Snippet [24.852728097115744]
  Multi-person pose understanding from RGB involves three complex tasks: pose estimation, tracking and motion forecasting. Most existing works either focus on a single task or employ multi-stage approaches to solving multiple tasks separately. We propose Snipper, a unified framework to perform multi-person 3D pose estimation, tracking, and motion forecasting simultaneously in a single stage.
  arXiv  Detail & Related papers  (2022-07-09T18:42:14Z)
• Learning Behavior Representations Through Multi-Timescale Bootstrapping [8.543808476554695]
  We introduce Bootstrap Across Multiple Scales (BAMS), a multi-scale representation learning model for behavior. We first apply our method on a dataset of quadrupeds navigating in different terrain types, and show that our model captures the temporal complexity of behavior.
  arXiv  Detail & Related papers  (2022-06-14T17:57:55Z)
• Joint Spatial-Temporal and Appearance Modeling with Transformer for Multiple Object Tracking [59.79252390626194]
  We propose a novel solution named TransSTAM, which leverages Transformer to model both the appearance features of each object and the spatial-temporal relationships among objects. The proposed method is evaluated on multiple public benchmarks including MOT16, MOT17, and MOT20, and it achieves a clear performance improvement in both IDF1 and HOTA.
  arXiv  Detail & Related papers  (2022-05-31T01:19:18Z)
• P-STMO: Pre-Trained Spatial Temporal Many-to-One Model for 3D Human Pose Estimation [78.83305967085413]
  This paper introduces a novel Pre-trained Spatial Temporal Many-to-One (P-STMO) model for 2D-to-3D human pose estimation task. Our method outperforms state-of-the-art methods with fewer parameters and less computational overhead.
  arXiv  Detail & Related papers  (2022-03-15T04:00:59Z)
• baller2vec: A Multi-Entity Transformer For Multi-Agent Spatiotemporal Modeling [17.352818121007576]
  Multi-agenttemporal modeling is a challenging task from both an algorithmic design perspective and computational perspective. We introduce baller2vec, a multi-entity generalization of the standard Transformer that can simultaneously integrate information across entities and time. We test the effectiveness of baller2vec for multi-agenttemporal modeling by training it to perform two different basketball-related tasks.
  arXiv  Detail & Related papers  (2021-02-05T17:02:04Z)
• SMART: Simultaneous Multi-Agent Recurrent Trajectory Prediction [72.37440317774556]
  We propose advances that address two key challenges in future trajectory prediction. multimodality in both training data and predictions and constant time inference regardless of number of agents.
  arXiv  Detail & Related papers  (2020-07-26T08:17:10Z)
• A Spatial-Temporal Attentive Network with Spatial Continuity for Trajectory Prediction [74.00750936752418]
  We propose a novel model named spatial-temporal attentive network with spatial continuity (STAN-SC) First, spatial-temporal attention mechanism is presented to explore the most useful and important information. Second, we conduct a joint feature sequence based on the sequence and instant state information to make the generative trajectories keep spatial continuity.
  arXiv  Detail & Related papers  (2020-03-13T04:35:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.