WildGraph: Realistic Graph-based Trajectory Generation for Wildlife
- URL: http://arxiv.org/abs/2404.08068v1
- Date: Thu, 11 Apr 2024 18:13:21 GMT
- Title: WildGraph: Realistic Graph-based Trajectory Generation for Wildlife
- Authors: Ali Al-Lawati, Elsayed Eshra, Prasenjit Mitra,
- Abstract summary: Trajectory generation is an important task in movement studies.
It circumvents the privacy, ethical, and technical challenges of collecting real trajectories from the target population.
In this paper, we consider the problem of generating long-horizon trajectories, akin to wildlife migration, based on a small set of real samples.
- Score: 3.4688186440441893
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Trajectory generation is an important task in movement studies; it circumvents the privacy, ethical, and technical challenges of collecting real trajectories from the target population. In particular, real trajectories in the wildlife domain are scarce as a result of ethical and environmental constraints of the collection process. In this paper, we consider the problem of generating long-horizon trajectories, akin to wildlife migration, based on a small set of real samples. We propose a hierarchical approach to learn the global movement characteristics of the real dataset and recursively refine localized regions. Our solution, WildGraph, discretizes the geographic path into a prototype network of H3 (https://www.uber.com/blog/h3/) regions and leverages a recurrent variational auto-encoder to probabilistically generate paths over the regions, based on occupancy. WildGraph successfully generates realistic months-long trajectories using a sample size as small as 60. Experiments performed on two wildlife migration datasets demonstrate that our proposed method improves the generalization of the generated trajectories in comparison to existing work while achieving superior or comparable performance in several benchmark metrics. Our code is published on the following repository: \url{https://github.com/aliwister/wildgraph}.
Related papers
- Mitigating Label Noise on Graph via Topological Sample Selection [72.86862597508077]
We propose a $textitTopological Sample Selection$ (TSS) method that boosts the informative sample selection process in a graph by utilising topological information.
We theoretically prove that our procedure minimizes an upper bound of the expected risk under target clean distribution, and experimentally show the superiority of our method compared with state-of-the-art baselines.
arXiv Detail & Related papers (2024-03-04T11:24:51Z) - STAGE: Scalable and Traversability-Aware Graph based Exploration Planner
for Dynamically Varying Environments [6.267574471145217]
The framework is structured around a novel goal oriented graph representation, that consists of, i) the local sub-graph and ii) the global graph layer respectively.
The global graph is build in an efficient way, using node-edge information exchange only on overlapping regions of sequential sub-graphs.
The proposed scheme is able to handle scene changes, adaptively updating the obstructed part of the global graph from traversable to not-traversable.
arXiv Detail & Related papers (2024-02-04T17:05:27Z) - WildGEN: Long-horizon Trajectory Generation for Wildlife [3.8986045286948]
Trajectory generation is an important concern in pedestrian, vehicle, and wildlife movement studies.
We introduce WildGEN: a conceptual framework that addresses this challenge by employing a Variational Auto-encoders (VAEs) based method.
A subsequent post-processing step of the generated trajectories is performed based on smoothing filters to reduce excessive wandering.
arXiv Detail & Related papers (2023-12-30T05:08:28Z) - GraNet: A Multi-Level Graph Network for 6-DoF Grasp Pose Generation in
Cluttered Scenes [0.5755004576310334]
GraNet is a graph-based grasp pose generation framework that translates a point cloud scene into multi-level graphs.
Our pipeline can thus characterize the spatial distribution of grasps in cluttered scenes, leading to a higher rate of effective grasping.
Our method achieves state-of-the-art performance on the large-scale GraspNet-1Billion benchmark, especially in grasping unseen objects.
arXiv Detail & Related papers (2023-12-06T08:36:29Z) - GeoNet: Benchmarking Unsupervised Adaptation across Geographies [71.23141626803287]
We study the problem of geographic robustness and make three main contributions.
First, we introduce a large-scale dataset GeoNet for geographic adaptation.
Second, we hypothesize that the major source of domain shifts arise from significant variations in scene context.
Third, we conduct an extensive evaluation of several state-of-the-art unsupervised domain adaptation algorithms and architectures.
arXiv Detail & Related papers (2023-03-27T17:59:34Z) - Optimal Propagation for Graph Neural Networks [51.08426265813481]
We propose a bi-level optimization approach for learning the optimal graph structure.
We also explore a low-rank approximation model for further reducing the time complexity.
arXiv Detail & Related papers (2022-05-06T03:37:00Z) - Learning Continuous Environment Fields via Implicit Functions [144.4913852552954]
We propose a novel scene representation that encodes reaching distance -- the distance between any position in the scene to a goal along a feasible trajectory.
We demonstrate that this environment field representation can directly guide the dynamic behaviors of agents in 2D mazes or 3D indoor scenes.
arXiv Detail & Related papers (2021-11-27T22:36:58Z) - TraND: Transferable Neighborhood Discovery for Unsupervised Cross-domain
Gait Recognition [77.77786072373942]
This paper proposes a Transferable Neighborhood Discovery (TraND) framework to bridge the domain gap for unsupervised cross-domain gait recognition.
We design an end-to-end trainable approach to automatically discover the confident neighborhoods of unlabeled samples in the latent space.
Our method achieves state-of-the-art results on two public datasets, i.e., CASIA-B and OU-LP.
arXiv Detail & Related papers (2021-02-09T03:07:07Z) - $n$-Reference Transfer Learning for Saliency Prediction [73.17061116358036]
We propose a few-shot transfer learning paradigm for saliency prediction.
The proposed framework is gradient-based and model-agnostic.
The results show that the proposed framework achieves a significant performance improvement.
arXiv Detail & Related papers (2020-07-09T23:20:44Z)
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.