Quantifying the Complexity of Standard Benchmarking Datasets for Long-Term Human Trajectory Prediction

• URL: http://arxiv.org/abs/2005.13934v4
• Date: Thu, 20 May 2021 08:17:40 GMT
• Title: Quantifying the Complexity of Standard Benchmarking Datasets for Long-Term Human Trajectory Prediction
• Authors: Ronny Hug, Stefan Becker, Wolfgang H\"ubner, Michael Arens
• Abstract summary: We propose an approach for quantifying the amount of information contained in a dataset from a prototype-based dataset representation. A large-scale complexity analysis is conducted on several human trajectory prediction benchmarking datasets.
• Score: 8.870188183999852
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Methods to quantify the complexity of trajectory datasets are still a missing piece in benchmarking human trajectory prediction models. In order to gain a better understanding of the complexity of trajectory prediction tasks and following the intuition, that more complex datasets contain more information, an approach for quantifying the amount of information contained in a dataset from a prototype-based dataset representation is proposed. The dataset representation is obtained by first employing a non-trivial spatial sequence alignment, which enables a subsequent learning vector quantization (LVQ) stage. A large-scale complexity analysis is conducted on several human trajectory prediction benchmarking datasets, followed by a brief discussion on indications for human trajectory prediction and benchmarking.

Related papers

• DACO: Towards Application-Driven and Comprehensive Data Analysis via Code Generation [83.30006900263744]
  Data analysis is a crucial analytical process to generate in-depth studies and conclusive insights. We propose to automatically generate high-quality answer annotations leveraging the code-generation capabilities of LLMs. Our DACO-RL algorithm is evaluated by human annotators to produce more helpful answers than SFT model in 57.72% cases.
  arXiv  Detail & Related papers  (2024-03-04T22:47:58Z)
• Minimally Supervised Learning using Topological Projections in Self-Organizing Maps [55.31182147885694]
  We introduce a semi-supervised learning approach based on topological projections in self-organizing maps (SOMs) Our proposed method first trains SOMs on unlabeled data and then a minimal number of available labeled data points are assigned to key best matching units (BMU) Our results indicate that the proposed minimally supervised model significantly outperforms traditional regression techniques.
  arXiv  Detail & Related papers  (2024-01-12T22:51:48Z)
• Pre-training on Synthetic Driving Data for Trajectory Prediction [61.520225216107306]
  We propose a pipeline-level solution to mitigate the issue of data scarcity in trajectory forecasting. We adopt HD map augmentation and trajectory synthesis for generating driving data, and then we learn representations by pre-training on them. We conduct extensive experiments to demonstrate the effectiveness of our data expansion and pre-training strategies.
  arXiv  Detail & Related papers  (2023-09-18T19:49:22Z)
• Dataset Complexity Assessment Based on Cumulative Maximum Scaled Area Under Laplacian Spectrum [38.65823547986758]
  It is meaningful to predict classification performance by assessing the complexity of datasets effectively before training DCNN models. This paper proposes a novel method called cumulative maximum scaled Area Under Laplacian Spectrum (cmsAULS)
  arXiv  Detail & Related papers  (2022-09-29T13:02:04Z)
• MEAT: Maneuver Extraction from Agent Trajectories [9.919575841909962]
  We propose an automated methodology that allows to extract maneuvers from agent trajectories in large-scale datasets. Although it is possible to use the resulting maneuvers for training classification networks, we exemplary use them for extensive trajectory dataset analysis.
  arXiv  Detail & Related papers  (2022-06-10T14:56:32Z)
• Representation Learning for Sequence Data with Deep Autoencoding Predictive Components [96.42805872177067]
  We propose a self-supervised representation learning method for sequence data, based on the intuition that useful representations of sequence data should exhibit a simple structure in the latent space. We encourage this latent structure by maximizing an estimate of predictive information of latent feature sequences, which is the mutual information between past and future windows at each time step. We demonstrate that our method recovers the latent space of noisy dynamical systems, extracts predictive features for forecasting tasks, and improves automatic speech recognition when used to pretrain the encoder on large amounts of unlabeled data.
  arXiv  Detail & Related papers  (2020-10-07T03:34:01Z)
• OpenTraj: Assessing Prediction Complexity in Human Trajectories Datasets [5.219568203653524]
  Human Trajectory Prediction (HTP) has gained much momentum in the last years and many solutions have been proposed to solve it. This paper addresses the question of evaluating how complex is a given dataset with respect to the prediction problem. For assessing a dataset complexity, we define a series of indicators around three concepts: Trajectory predictability; Trajectory regularity; Context complexity.
  arXiv  Detail & Related papers  (2020-10-02T09:37:18Z)
• Evaluating representations by the complexity of learning low-loss predictors [55.94170724668857]
  We consider the problem of evaluating representations of data for use in solving a downstream task. We propose to measure the quality of a representation by the complexity of learning a predictor on top of the representation that achieves low loss on a task of interest.
  arXiv  Detail & Related papers  (2020-09-15T22:06:58Z)
• A Short Note on Analyzing Sequence Complexity in Trajectory Prediction Benchmarks [8.870188183999852]
  An approach for determining a dataset representation in terms of a small set of distinguishable sub-sequences is proposed. A first proof of concept on synthetically generated and real-world datasets shows the viability of the approach.
  arXiv  Detail & Related papers  (2020-03-27T11:44:11Z)
• CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus [62.86856923633923]
  We present a robust estimator for fitting multiple parametric models of the same form to noisy measurements. In contrast to previous works, which resorted to hand-crafted search strategies for multiple model detection, we learn the search strategy from data. For self-supervised learning of the search, we evaluate the proposed algorithm on multi-homography estimation and demonstrate an accuracy that is superior to state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-01-08T17:37:01Z)
• Adaptive Discrete Smoothing for High-Dimensional and Nonlinear Panel Data [4.550919471480445]
  We develop a data-driven smoothing technique for high-dimensional and non-linear panel data models. The weights are determined by a data-driven way and depend on the similarity between the corresponding functions. We conduct a simulation study which shows that the prediction can be greatly improved by using our estimator.
  arXiv  Detail & Related papers  (2019-12-30T09:50:58Z)

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.