Pattern Ensembling for Spatial Trajectory Reconstruction

• URL: http://arxiv.org/abs/2101.09844v1
• Date: Mon, 25 Jan 2021 01:44:00 GMT
• Title: Pattern Ensembling for Spatial Trajectory Reconstruction
• Authors: Shivam Pathak, Mingyi He, Sergey Malinchik, Stanislav Sobolevsky
• Abstract summary: We propose a method to use similar trajectory patterns from the local vicinity to robustly reconstruct missing or unreliable observations. By effectively leveraging the similarities in real-world trajectories, our pattern ensembling method helps to reconstruct missing trajectory segments of extended length and complex geometry.
• Score: 1.1087735229999818
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Digital sensing provides an unprecedented opportunity to assess and understand mobility. However, incompleteness, missing information, possible inaccuracies, and temporal heterogeneity in the geolocation data can undermine its applicability. As mobility patterns are often repeated, we propose a method to use similar trajectory patterns from the local vicinity and probabilistically ensemble them to robustly reconstruct missing or unreliable observations. We evaluate the proposed approach in comparison with traditional functional trajectory interpolation using a case of sea vessel trajectory data provided by The Automatic Identification System (AIS). By effectively leveraging the similarities in real-world trajectories, our pattern ensembling method helps to reconstruct missing trajectory segments of extended length and complex geometry. It can be used for locating mobile objects when temporary unobserved as well as for creating an evenly sampled trajectory interpolation useful for further trajectory mining.

Related papers

• PLMTrajRec: A Scalable and Generalizable Trajectory Recovery Method with Pre-trained Language Models [31.98187852227127]
  We propose a novel trajectory recovery model called PLMTrajRec. It leverages the scalability of a pre-trained language model (PLM) and can be fine-tuned with only a limited set of dense trajectories. To handle different sampling intervals in sparse trajectories, we first convert each trajectory's sampling interval and movement features into natural language representations.
  arXiv  Detail & Related papers  (2024-10-18T08:38:12Z)
• Trajectory Anomaly Detection with Language Models [21.401931052512595]
  This paper presents a novel approach for trajectory anomaly detection using an autoregressive causal-attention model, termed LM-TAD. By treating trajectories as sequences of tokens, our model learns the probability distributions over trajectories, enabling the identification of anomalous locations with high precision. Our experiments demonstrate the effectiveness of LM-TAD on both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2024-09-18T17:33:31Z)
• Efficient Data Representation for Motion Forecasting: A Scene-Specific Trajectory Set Approach [12.335528093380631]
  This study introduces a novel approach for generating scene-specific trajectory sets tailored to different contexts. A deterministic goal sampling algorithm identifies relevant map regions, while our Recursive In-Distribution Subsampling (RIDS) method enhances trajectory plausibility. Experiments on the Argoverse 2 dataset demonstrate that our method achieves up to a 10% improvement in Driving Area Compliance.
  arXiv  Detail & Related papers  (2024-07-30T11:06:39Z)
• Holistic Representation Learning for Multitask Trajectory Anomaly Detection [65.72942351514956]
  We propose a holistic representation of skeleton trajectories to learn expected motions across segments at different times. We encode temporally occluded trajectories, jointly learn latent representations of the occluded segments, and reconstruct trajectories based on expected motions across different temporal segments.
  arXiv  Detail & Related papers  (2023-11-03T11:32:53Z)
• Latent Traversals in Generative Models as Potential Flows [113.4232528843775]
  We propose to model latent structures with a learned dynamic potential landscape. Inspired by physics, optimal transport, and neuroscience, these potential landscapes are learned as physically realistic partial differential equations. Our method achieves both more qualitatively and quantitatively disentangled trajectories than state-of-the-art baselines.
  arXiv  Detail & Related papers  (2023-04-25T15:53:45Z)
• DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model [44.490978394267195]
  We propose a spatial-temporal probabilistic model for trajectory generation (DiffTraj) The core idea is to reconstruct and synthesize geographic trajectories from white noise through a reverse trajectory denoising process. Experiments on two real-world datasets show that DiffTraj can be intuitively applied to generate high-fidelity trajectories.
  arXiv  Detail & Related papers  (2023-04-23T08:42:45Z)
• VTAE: Variational Transformer Autoencoder with Manifolds Learning [144.0546653941249]
  Deep generative models have demonstrated successful applications in learning non-linear data distributions through a number of latent variables. The nonlinearity of the generator implies that the latent space shows an unsatisfactory projection of the data space, which results in poor representation learning. We show that geodesics and accurate computation can substantially improve the performance of deep generative models.
  arXiv  Detail & Related papers  (2023-04-03T13:13:19Z)
• Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction [60.60223171143206]
  Trajectory prediction is a crucial undertaking in understanding entity movement or human behavior from observed sequences. Current methods often assume that the observed sequences are complete while ignoring the potential for missing values. This paper presents a unified framework, the Graph-based Conditional Variational Recurrent Neural Network (GC-VRNN), which can perform trajectory imputation and prediction simultaneously.
  arXiv  Detail & Related papers  (2023-03-28T14:27:27Z)
• Temporally-Coherent Surface Reconstruction via Metric-Consistent Atlases [131.50372468579067]
  We represent the reconstructed surface as an atlas, using a neural network. We empirically show that our method achieves results that exceed that state of the art in the accuracy of unsupervised correspondences and accuracy of surface reconstruction.
  arXiv  Detail & Related papers  (2021-04-14T16:21:22Z)
• Map-Based Temporally Consistent Geolocalization through Learning Motion Trajectories [0.5076419064097732]
  We propose a novel trajectory learning method that exploits motion trajectories on topological map using recurrent neural network. Inspired by human's ability to both be aware of distance and direction of self-motion in navigation, our trajectory learning method learns a pattern representation of trajectories encoded as a sequence of distances and turning angles to assist self-localization.
  arXiv  Detail & Related papers  (2020-10-13T02:08:45Z)
• Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for Online Collision Avoidance [95.86944752753564]
  We propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties. Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates. The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms.
  arXiv  Detail & Related papers  (2020-07-28T07:34:30Z)

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.