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Found 12 papers, 0 papers with code
Generating Synthetic Ground Truth Distributions for Multi-step Trajectory Prediction using Probabilistic Composite Bézier Curves
An appropriate data basis grants one of the most important aspects for training and evaluating probabilistic trajectory prediction models based on neural networks.
Utilizing dataset affinity prediction in object detection to assess training data
Data pooling offers various advantages, such as increasing the sample size, improving generalization, reducing sampling bias, and addressing data sparsity and quality, but it is not straightforward and may even be counterproductive.
Bézier Curve Gaussian Processes
Probabilistic models for sequential data are the basis for a variety of applications concerned with processing timely ordered information.
Generating Synthetic Training Data for Deep Learning-Based UAV Trajectory Prediction
To demonstrate the applicability of the synthetic trajectory data, we show that an RNN-based prediction model solely trained on the generated data can outperform classic reference models on a real-world UAV tracking dataset.
MissFormer: (In-)attention-based handling of missing observations for trajectory filtering and prediction
By providing missing tokens, binary-encoded missing events, the model learns to in-attend to missing data and infers a complete trajectory conditioned on the remaining inputs.
Handling Missing Observations with an RNN-based Prediction-Update Cycle
For providing a full temporal filtering cycle, a basic RNN is extended to take observations and the associated belief about its accuracy into account for updating the current state.
Quantifying the Complexity of Standard Benchmarking Datasets for Long-Term Human Trajectory Prediction
Methods to quantify the complexity of trajectory datasets are still a missing piece in benchmarking human trajectory prediction models.
A Short Note on Analyzing Sequence Complexity in Trajectory Prediction Benchmarks
The analysis and quantification of sequence complexity is an open problem frequently encountered when defining trajectory prediction benchmarks.
Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures
Towards this end, a neural network model for continuous-time stochastic processes usable for sequence prediction is proposed.
An RNN-based IMM Filter Surrogate
The problem of varying dynamics of tracked objects, such as pedestrians, is traditionally tackled with approaches like the Interacting Multiple Model (IMM) filter using a Bayesian formulation.
An Evaluation of Trajectory Prediction Approaches and Notes on the TrajNet Benchmark
In recent years, there is a shift from modeling the tracking problem based on Bayesian formulation towards using deep neural networks.
Particle-based pedestrian path prediction using LSTM-MDL models
Recurrent neural networks are able to learn complex long-term relationships from sequential data and output a pdf over the state space.
