https://github.com/vincent-leguen/DILATE
공식 코드Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models
Papers with Code 아카이브
Trajectory Forecasting
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Trajectory forecasting is a sequential prediction task, where a forecasting model predicts future trajectories of all moving agents (humans, vehicles, etc.) in a scene, based on their past trajectories and/or the scene context.
(Illustrative figure from Social NCE: Contrastive Learning of Socially-aware Motion Representations)
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/vincent-leguen/STDL
공식 코드Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models
https://github.com/vita-epfl/jrdb-traj
공식 코드JRDB-Traj: A Dataset and Benchmark for Trajectory Forecasting in Crowds
https://github.com/vita-epfl/social-nce
공식 코드Social NCE: Contrastive Learning of Socially-aware Motion Representations
https://github.com/wtliao/DCENet
공식 코드Exploring Dynamic Context for Multi-path Trajectory Prediction
https://github.com/xiaolongtang23/hpnet
공식 코드HPNet: Dynamic Trajectory Forecasting with Historical Prediction Attention
https://github.com/xuedashuai/hmnet
공식 코드Hierarchical Motion Encoder-Decoder Network for Trajectory Forecasting
https://github.com/yhli123/pretraining_on_synthetic_driving_data_for_trajectory_prediction
공식 코드Pre-training on Synthetic Driving Data for Trajectory Prediction
https://github.com/ziqipang/streamingforecasting
공식 코드https://github.com/zl-helios/trajpac
공식 코드TrajPAC: Towards Robustness Verification of Pedestrian Trajectory Prediction Models
https://github.com/ApeironY/PCCSNet
Three Steps to Multimodal Trajectory Prediction: Modality Clustering, Classification and Synthesis
https://github.com/FunmiKesa/JLA
Joint Learning Architecture for Multiple Object Tracking and Trajectory Forecasting
https://github.com/JunweiLiang/social-distancing-prediction
Peeking into the Future: Predicting Future Person Activities and Locations in Videos
https://github.com/KrishnaswamyLab/ImageFlowNet
https://github.com/SeongjuLee/DCENet-PyTorch
Exploring Dynamic Context for Multi-path Trajectory Prediction
https://github.com/YuejiangLIU/social-nce-trajectron-plus-plus
Social NCE: Contrastive Learning of Socially-aware Motion Representations
https://github.com/amiryanj/socialways
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
https://github.com/argoverse/argoverse-api
https://github.com/cmubig/SPEC
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
https://github.com/daeheepark/t4p
trajdata: A Unified Interface to Multiple Human Trajectory Datasets
https://github.com/harshayugirase/human-path-prediction
It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction
https://github.com/huang-xx/STGAT
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
https://github.com/inhwanbae/npsn
https://github.com/johnwlambert/argoverse_cbgs_kf_tracker
https://github.com/m-hasan-n/pooling
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
https://github.com/marcdemers/batch-DILATE
Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models
https://github.com/mirkozaff/aa-sgan
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
https://github.com/nvlabs/adaptive-prediction
Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data
https://github.com/nvr-avg/adaptive-prediction
Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data
https://github.com/nvr-avg/trajdata
trajdata: A Unified Interface to Multiple Human Trajectory Datasets
https://github.com/olly-styles/Dynamic-Trajectory-Predictor
Forecasting Pedestrian Trajectory with Machine-Annotated Training Data
https://github.com/omron-sinicx/planning-datasets
OpenTraj: Assessing Prediction Complexity in Human Trajectories Datasets
https://github.com/pranav-chib/trajimpute
EigenTrajectory: Low-Rank Descriptors for Multi-Modal Trajectory Forecasting
https://github.com/qiyan98/social-nce-stgcnn
Social NCE: Contrastive Learning of Socially-aware Motion Representations
https://github.com/rohanchandra30/TrackNPred
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
https://github.com/romi514/TLSGAN
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.