Multi-Object Tracking based on Imaging Radar 3D Object Detection

• URL: http://arxiv.org/abs/2406.01011v1
• Date: Mon, 3 Jun 2024 05:46:23 GMT
• Title: Multi-Object Tracking based on Imaging Radar 3D Object Detection
• Authors: Patrick Palmer, Martin Krüger, Richard Altendorfer, Torsten Bertram,
• Abstract summary: This paper presents an approach for tracking surrounding traffic participants with a classical tracking algorithm. Learning based object detectors have been shown to work adequately on lidar and camera data, while learning based object detectors using standard radar data input have proven to be inferior. With the improvements to radar sensor technology in the form of imaging radars, the object detection performance on radar was greatly improved but is still limited compared to lidar sensors due to the sparsity of the radar point cloud. The tracking algorithm must overcome the limited detection quality while generating consistent tracks.
• Score: 0.13499500088995461
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Effective tracking of surrounding traffic participants allows for an accurate state estimation as a necessary ingredient for prediction of future behavior and therefore adequate planning of the ego vehicle trajectory. One approach for detecting and tracking surrounding traffic participants is the combination of a learning based object detector with a classical tracking algorithm. Learning based object detectors have been shown to work adequately on lidar and camera data, while learning based object detectors using standard radar data input have proven to be inferior. Recently, with the improvements to radar sensor technology in the form of imaging radars, the object detection performance on radar was greatly improved but is still limited compared to lidar sensors due to the sparsity of the radar point cloud. This presents a unique challenge for the task of multi-object tracking. The tracking algorithm must overcome the limited detection quality while generating consistent tracks. To this end, a comparison between different multi-object tracking methods on imaging radar data is required to investigate its potential for downstream tasks. The work at hand compares multiple approaches and analyzes their limitations when applied to imaging radar data. Furthermore, enhancements to the presented approaches in the form of probabilistic association algorithms are considered for this task.

Related papers

• LEROjD: Lidar Extended Radar-Only Object Detection [0.22870279047711525]
  3+1D imaging radar sensors offer a cost-effective, robust alternative to lidar. Although lidar should not be used during inference, it can aid the training of radar-only object detectors. We explore two strategies to transfer knowledge from the lidar to the radar domain and radar-only object detectors.
  arXiv  Detail & Related papers  (2024-09-09T12:43:25Z)
• OOSTraj: Out-of-Sight Trajectory Prediction With Vision-Positioning Denoising [49.86409475232849]
  Trajectory prediction is fundamental in computer vision and autonomous driving. Existing approaches in this field often assume precise and complete observational data. We present a novel method for out-of-sight trajectory prediction that leverages a vision-positioning technique.
  arXiv  Detail & Related papers  (2024-04-02T18:30:29Z)
• Leveraging Self-Supervised Instance Contrastive Learning for Radar Object Detection [7.728838099011661]
  This paper presents RiCL, an instance contrastive learning framework to pre-train radar object detectors. We aim to pre-train an object detector's backbone, head and neck to learn with fewer data.
  arXiv  Detail & Related papers  (2024-02-13T12:53:33Z)
• ROFusion: Efficient Object Detection using Hybrid Point-wise Radar-Optical Fusion [14.419658061805507]
  We propose a hybrid point-wise Radar-Optical fusion approach for object detection in autonomous driving scenarios. The framework benefits from dense contextual information from both the range-doppler spectrum and images which are integrated to learn a multi-modal feature representation.
  arXiv  Detail & Related papers  (2023-07-17T04:25:46Z)
• Semantic Segmentation of Radar Detections using Convolutions on Point Clouds [59.45414406974091]
  We introduce a deep-learning based method to convolve radar detections into point clouds. We adapt this algorithm to radar-specific properties through distance-dependent clustering and pre-processing of input point clouds. Our network outperforms state-of-the-art approaches that are based on PointNet++ on the task of semantic segmentation of radar point clouds.
  arXiv  Detail & Related papers  (2023-05-22T07:09:35Z)
• R4Dyn: Exploring Radar for Self-Supervised Monocular Depth Estimation of Dynamic Scenes [69.6715406227469]
  Self-supervised monocular depth estimation in driving scenarios has achieved comparable performance to supervised approaches. We present R4Dyn, a novel set of techniques to use cost-efficient radar data on top of a self-supervised depth estimation framework.
  arXiv  Detail & Related papers  (2021-08-10T17:57:03Z)
• CFTrack: Center-based Radar and Camera Fusion for 3D Multi-Object Tracking [9.62721286522053]
  We propose an end-to-end network for joint object detection and tracking based on radar and camera sensor fusion. Our proposed method uses a center-based radar-camera fusion algorithm for object detection and utilizes a greedy algorithm for object association. We evaluate our method on the challenging nuScenes dataset, where it achieves 20.0 AMOTA and outperforms all vision-based 3D tracking methods in the benchmark.
  arXiv  Detail & Related papers  (2021-07-11T23:56:53Z)
• Radar Artifact Labeling Framework (RALF): Method for Plausible Radar Detections in Datasets [2.5899040911480187]
  We propose a cross sensor Radar Artifact Labeling Framework (RALF) for labeling sparse radar point clouds. RALF provides plausibility labels for radar raw detections, distinguishing between artifacts and targets. We validate the results by evaluating error metrics on semi-manually labeled ground truth dataset of $3.28cdot106$ points.
  arXiv  Detail & Related papers  (2020-12-03T15:11:31Z)
• LiRaNet: End-to-End Trajectory Prediction using Spatio-Temporal Radar Fusion [52.59664614744447]
  We present LiRaNet, a novel end-to-end trajectory prediction method which utilizes radar sensor information along with widely used lidar and high definition (HD) maps. automotive radar provides rich, complementary information, allowing for longer range vehicle detection as well as instantaneous velocity measurements.
  arXiv  Detail & Related papers  (2020-10-02T00:13:00Z)
• Perceiving Traffic from Aerial Images [86.994032967469]
  We propose an object detection method called Butterfly Detector that is tailored to detect objects in aerial images. We evaluate our Butterfly Detector on two publicly available UAV datasets (UAVDT and VisDrone 2019) and show that it outperforms previous state-of-the-art methods while remaining real-time.
  arXiv  Detail & Related papers  (2020-09-16T11:37:43Z)
• RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects [73.80316195652493]
  We tackle the problem of exploiting Radar for perception in the context of self-driving cars. We propose a new solution that exploits both LiDAR and Radar sensors for perception. Our approach, dubbed RadarNet, features a voxel-based early fusion and an attention-based late fusion.
  arXiv  Detail & Related papers  (2020-07-28T17:15:02Z)

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.