Valeo Near-Field: a novel dataset for pedestrian intent detection

• URL: http://arxiv.org/abs/2510.15673v1
• Date: Fri, 17 Oct 2025 14:02:54 GMT
• Title: Valeo Near-Field: a novel dataset for pedestrian intent detection
• Authors: Antonyo Musabini, Rachid Benmokhtar, Jagdish Bhanushali, Victor Galizzi, Bertrand Luvison, Xavier Perrotton,
• Abstract summary: This paper presents a novel dataset aimed at detecting pedestrians' intentions as they approach an ego-vehicle.<n>The dataset comprises synchronized multi-modal data, including fisheye camera feeds, lidar laser scans, ultrasonic sensor readings, and motion capture-based 3D body poses.
• Score: 21.659078060884614
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper presents a novel dataset aimed at detecting pedestrians' intentions as they approach an ego-vehicle. The dataset comprises synchronized multi-modal data, including fisheye camera feeds, lidar laser scans, ultrasonic sensor readings, and motion capture-based 3D body poses, collected across diverse real-world scenarios. Key contributions include detailed annotations of 3D body joint positions synchronized with fisheye camera images, as well as accurate 3D pedestrian positions extracted from lidar data, facilitating robust benchmarking for perception algorithms. We release a portion of the dataset along with a comprehensive benchmark suite, featuring evaluation metrics for accuracy, efficiency, and scalability on embedded systems. By addressing real-world challenges such as sensor occlusions, dynamic environments, and hardware constraints, this dataset offers a unique resource for developing and evaluating state-of-the-art algorithms in pedestrian detection, 3D pose estimation and 4D trajectory and intention prediction. Additionally, we provide baseline performance metrics using custom neural network architectures and suggest future research directions to encourage the adoption and enhancement of the dataset. This work aims to serve as a foundation for researchers seeking to advance the capabilities of intelligent vehicles in near-field scenarios.

Related papers

• RS3DBench: A Comprehensive Benchmark for 3D Spatial Perception in Remote Sensing [71.75704516333394]
  We present a visual Benchmark for 3D understanding of Remotely Sensed images, dubbed RS3DBench.<n>This dataset encompasses 54,951 pairs of remote sensing images and pixel-level aligned depth maps.<n>We introduce a remotely sensed depth estimation model derived from stable diffusion, harnessing its multimodal fusion capabilities.
  arXiv  Detail & Related papers  (2025-09-23T11:20:51Z)
• S-LAM3D: Segmentation-Guided Monocular 3D Object Detection via Feature Space Fusion [0.0]
  Monocular 3D Object Detection represents a challenging Computer Vision task due to the nature of the input used.<n>We introduce a decoupled strategy based on injecting precomputed segmentation information priors and fusing them directly into the feature space for guiding the detection.<n>The proposed method is evaluated on the KITTI 3D Object Detection Benchmark, outperforming the equivalent architecture that relies only on RGB image features for small objects in the scene.
  arXiv  Detail & Related papers  (2025-09-07T10:14:56Z)
• Multi-Modal Dataset Acquisition for Photometrically Challenging Object [56.30027922063559]
  This paper addresses the limitations of current datasets for 3D vision tasks in terms of accuracy, size, realism, and suitable imaging modalities for photometrically challenging objects. We propose a novel annotation and acquisition pipeline that enhances existing 3D perception and 6D object pose datasets.
  arXiv  Detail & Related papers  (2023-08-21T10:38:32Z)
• AGO-Net: Association-Guided 3D Point Cloud Object Detection Network [86.10213302724085]
  We propose a novel 3D detection framework that associates intact features for objects via domain adaptation. We achieve new state-of-the-art performance on the KITTI 3D detection benchmark in both accuracy and speed.
  arXiv  Detail & Related papers  (2022-08-24T16:54:38Z)
• Semi-Perspective Decoupled Heatmaps for 3D Robot Pose Estimation from Depth Maps [66.24554680709417]
  Knowing the exact 3D location of workers and robots in a collaborative environment enables several real applications. We propose a non-invasive framework based on depth devices and deep neural networks to estimate the 3D pose of robots from an external camera.
  arXiv  Detail & Related papers  (2022-07-06T08:52:12Z)
• Multi-Camera Multiple 3D Object Tracking on the Move for Autonomous Vehicles [17.12321292167318]
  It is important for object detection and tracking to address new challenges, such as achieving consistent results across views of cameras. This work presents a new Global Association Graph Model with Link Prediction approach to predict existing tracklets location and link detections with tracklets. Our model exploits to improve the detection accuracy of a standard 3D object detector in the nuScenes detection challenge.
  arXiv  Detail & Related papers  (2022-04-19T22:50:36Z)
• Monocular Quasi-Dense 3D Object Tracking [99.51683944057191]
  A reliable and accurate 3D tracking framework is essential for predicting future locations of surrounding objects and planning the observer's actions in numerous applications such as autonomous driving. We propose a framework that can effectively associate moving objects over time and estimate their full 3D bounding box information from a sequence of 2D images captured on a moving platform.
  arXiv  Detail & Related papers  (2021-03-12T15:30:02Z)
• InfoFocus: 3D Object Detection for Autonomous Driving with Dynamic Information Modeling [65.47126868838836]
  We propose a novel 3D object detection framework with dynamic information modeling. Coarse predictions are generated in the first stage via a voxel-based region proposal network. Experiments are conducted on the large-scale nuScenes 3D detection benchmark.
  arXiv  Detail & Related papers  (2020-07-16T18:27:08Z)

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.