An Efficient Semi-Automated Scheme for Infrastructure LiDAR Annotation

• URL: http://arxiv.org/abs/2301.10732v1
• Date: Wed, 25 Jan 2023 17:42:15 GMT
• Title: An Efficient Semi-Automated Scheme for Infrastructure LiDAR Annotation
• Authors: Aotian Wu, Pan He, Xiao Li, Ke Chen, Sanjay Ranka, Anand Rangarajan
• Abstract summary: We present an efficient semi-automated annotation tool that automatically annotates LiDAR sequences with tracking algorithms. Our tool seamlessly integrates multi-object tracking (MOT), single-object tracking (SOT) and suitable trajectory post-processing techniques.
• Score: 15.523875367380196
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Most existing perception systems rely on sensory data acquired from cameras, which perform poorly in low light and adverse weather conditions. To resolve this limitation, we have witnessed advanced LiDAR sensors become popular in perception tasks in autonomous driving applications. Nevertheless, their usage in traffic monitoring systems is less ubiquitous. We identify two significant obstacles in cost-effectively and efficiently developing such a LiDAR-based traffic monitoring system: (i) public LiDAR datasets are insufficient for supporting perception tasks in infrastructure systems, and (ii) 3D annotations on LiDAR point clouds are time-consuming and expensive. To fill this gap, we present an efficient semi-automated annotation tool that automatically annotates LiDAR sequences with tracking algorithms while offering a fully annotated infrastructure LiDAR dataset -- FLORIDA (Florida LiDAR-based Object Recognition and Intelligent Data Annotation) -- which will be made publicly available. Our advanced annotation tool seamlessly integrates multi-object tracking (MOT), single-object tracking (SOT), and suitable trajectory post-processing techniques. Specifically, we introduce a human-in-the-loop schema in which annotators recursively fix and refine annotations imperfectly predicted by our tool and incrementally add them to the training dataset to obtain better SOT and MOT models. By repeating the process, we significantly increase the overall annotation speed by three to four times and obtain better qualitative annotations than a state-of-the-art annotation tool. The human annotation experiments verify the effectiveness of our annotation tool. In addition, we provide detailed statistics and object detection evaluation results for our dataset in serving as a benchmark for perception tasks at traffic intersections.

Related papers

• TLD-READY: Traffic Light Detection -- Relevance Estimation and Deployment Analysis [9.458657306918859]
  Effective traffic light detection is a critical component of the perception stack in autonomous vehicles. This work introduces a novel deep-learning detection system while addressing the challenges of previous work. We propose a relevance estimation system that innovatively uses directional arrow markings on the road, eliminating the need for prior map creation.
  arXiv  Detail & Related papers  (2024-09-11T14:12:44Z)
• Multi-Modal Data-Efficient 3D Scene Understanding for Autonomous Driving [58.16024314532443]
  We introduce LaserMix++, a framework that integrates laser beam manipulations from disparate LiDAR scans and incorporates LiDAR-camera correspondences to assist data-efficient learning. Results demonstrate that LaserMix++ outperforms fully supervised alternatives, achieving comparable accuracy with five times fewer annotations. This substantial advancement underscores the potential of semi-supervised approaches in reducing the reliance on extensive labeled data in LiDAR-based 3D scene understanding systems.
  arXiv  Detail & Related papers  (2024-05-08T17:59:53Z)
• 3D Object Detection and High-Resolution Traffic Parameters Extraction Using Low-Resolution LiDAR Data [14.142956899468922]
  This study proposes an innovative framework that alleviates the need for multiple LiDAR systems and simplifies the laborious 3D annotation process. Using the 2D bounding box detection and extracted height information, this study is able to generate 3D bounding boxes automatically without human intervention.
  arXiv  Detail & Related papers  (2024-01-13T01:22:20Z)
• Refining the ONCE Benchmark with Hyperparameter Tuning [45.55545585587993]
  This work focuses on the evaluation of semi-supervised learning approaches for point cloud data. Data annotation is of paramount importance in the context of LiDAR applications. We show that improvements from previous semi-supervised methods may not be as profound as previously thought.
  arXiv  Detail & Related papers  (2023-11-10T13:39:07Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• Efficient and Robust LiDAR-Based End-to-End Navigation [132.52661670308606]
  We present an efficient and robust LiDAR-based end-to-end navigation framework. We propose Fast-LiDARNet that is based on sparse convolution kernel optimization and hardware-aware model design. We then propose Hybrid Evidential Fusion that directly estimates the uncertainty of the prediction from only a single forward pass.
  arXiv  Detail & Related papers  (2021-05-20T17:52:37Z)
• Learnable Online Graph Representations for 3D Multi-Object Tracking [156.58876381318402]
  We propose a unified and learning based approach to the 3D MOT problem. We employ a Neural Message Passing network for data association that is fully trainable. We show the merit of the proposed approach on the publicly available nuScenes dataset by achieving state-of-the-art performance of 65.6% AMOTA and 58% fewer ID-switches.
  arXiv  Detail & Related papers  (2021-04-23T17:59:28Z)
• FLAVA: Find, Localize, Adjust and Verify to Annotate LiDAR-Based Point Clouds [93.3595555830426]
  We propose FLAVA, a systematic approach to minimizing human interaction in the annotation process. Specifically, we divide the annotation pipeline into four parts: find, localize, adjust and verify. Our system also greatly reduces the amount of interaction by introducing a light-weight yet effective mechanism to propagate the results.
  arXiv  Detail & Related papers  (2020-11-20T02:22:36Z)

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.