Trajectory Modeling
16 papers with code • 1 benchmarks • 6 datasets
The equivalent of language modeling but for trajectories.
Benchmarks
These leaderboards are used to track progress in Trajectory Modeling
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
highD Dataset
inD Dataset
rounD Dataset
exiD Dataset
NBA SportVU
uniD Dataset
Most implemented papers
SIND: A Drone Dataset at Signalized Intersection in China
Then, the behaviors of traffic light violations in SIND are recorded.
Latent Variable Sequential Set Transformers For Joint Multi-Agent Motion Prediction
roggirg/AutoBots
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ICLR 2022
AutoBots can produce either the trajectory of one ego-agent or a distribution over the future trajectories for all agents in the scene.
Q-value Regularized Transformer for Offline Reinforcement Learning
charleshsc/qt
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Recent advancements in offline reinforcement learning (RL) have underscored the capabilities of Conditional Sequence Modeling (CSM), a paradigm that learns the action distribution based on history trajectory and target returns for each state.
The Trajectron: Probabilistic Multi-Agent Trajectory Modeling With Dynamic Spatiotemporal Graphs
StanfordASL/Trajectron
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ICCV 2019
Developing safe human-robot interaction systems is a necessary step towards the widespread integration of autonomous agents in society.
Multi-layer Trajectory Clustering: A Network Algorithm for Disease Subtyping
Many diseases display heterogeneity in clinical features and their progression, indicative of the existence of disease subtypes.
baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling Coordinated Agents
airalcorn2/baller2vecplusplus
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NeurIPS 2021
In many multi-agent spatiotemporal systems, agents operate under the influence of shared, unobserved variables (e. g., the play a team is executing in a game of basketball).
PTRAIL -- A python package for parallel trajectory data preprocessing
Trajectory data represent a trace of an object that changes its position in space over time.
Clustering of longitudinal data: A tutorial on a variety of approaches
During the past two decades, methods for identifying groups with different trends in longitudinal data have become of increasing interest across many areas of research.
Contrastive Trajectory Similarity Learning with Dual-Feature Attention
Trajectory similarity measures act as query predicates in trajectory databases, making them the key player in determining the query results.
Video-based marker-free tracking and multi-scale analysis of mouse locomotor activity and behavioral aspects in an open field arena: A perspective approach to the quantification of complex gait disturbances associated with Alzheimer's disease
Discussion: We expect that the proposed approach, due to its universality, robustness and clear physical interpretation, is a promising direction for the design of applied analysis tools for the diagnostics of various gait disturbances and behavioral aspects in animal models.
