Deep traffic light detection by overlaying synthetic context on arbitrary natural images

• URL: http://arxiv.org/abs/2011.03841v3
• Date: Thu, 10 Dec 2020 22:44:41 GMT
• Title: Deep traffic light detection by overlaying synthetic context on arbitrary natural images
• Authors: Jean Pablo Vieira de Mello, Lucas Tabelini, Rodrigo F. Berriel, Thiago M. Paix\~ao, Alberto F. de Souza, Claudine Badue, Nicu Sebe, Thiago Oliveira-Santos
• Abstract summary: We propose a method to generate artificial traffic-related training data for deep traffic light detectors. This data is generated using basic non-realistic computer graphics to blend fake traffic scenes on top of arbitrary image backgrounds. It also tackles the intrinsic data imbalance problem in traffic light datasets, caused mainly by the low amount of samples of the yellow state.
• Score: 49.592798832978296
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Deep neural networks come as an effective solution to many problems associated with autonomous driving. By providing real image samples with traffic context to the network, the model learns to detect and classify elements of interest, such as pedestrians, traffic signs, and traffic lights. However, acquiring and annotating real data can be extremely costly in terms of time and effort. In this context, we propose a method to generate artificial traffic-related training data for deep traffic light detectors. This data is generated using basic non-realistic computer graphics to blend fake traffic scenes on top of arbitrary image backgrounds that are not related to the traffic domain. Thus, a large amount of training data can be generated without annotation efforts. Furthermore, it also tackles the intrinsic data imbalance problem in traffic light datasets, caused mainly by the low amount of samples of the yellow state. Experiments show that it is possible to achieve results comparable to those obtained with real training data from the problem domain, yielding an average mAP and an average F1-score which are each nearly 4 p.p. higher than the respective metrics obtained with a real-world reference model.

Related papers

• IncidentNet: Traffic Incident Detection, Localization and Severity Estimation with Sparse Sensing [0.6787248655856052]
  IncidentNet is a novel approach for classifying, localizing, and estimating the severity of traffic incidents. Our model works on microscopic traffic data that can be collected using cameras installed at traffic intersections.
  arXiv  Detail & Related papers  (2024-08-02T04:09:15Z)
• BjTT: A Large-scale Multimodal Dataset for Traffic Prediction [49.93028461584377]
  Traditional traffic prediction methods rely on historical traffic data to predict traffic trends. In this work, we explore how generative models combined with text describing the traffic system can be applied for traffic generation. We propose ChatTraffic, the first diffusion model for text-to-traffic generation.
  arXiv  Detail & Related papers  (2024-03-08T04:19:56Z)
• Traffic Scene Parsing through the TSP6K Dataset [109.69836680564616]
  We introduce a specialized traffic monitoring dataset, termed TSP6K, with high-quality pixel-level and instance-level annotations. The dataset captures more crowded traffic scenes with several times more traffic participants than the existing driving scenes. We propose a detail refining decoder for scene parsing, which recovers the details of different semantic regions in traffic scenes.
  arXiv  Detail & Related papers  (2023-03-06T02:05:14Z)
• Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach [76.45949280328838]
  We propose a Laplacian enhanced low-rank tensor (LETC) framework featuring both lowrankness and multi-temporal correlations for large-scale traffic speed kriging. We then design an efficient solution algorithm via several effective numeric techniques to scale up the proposed model to network-wide kriging.
  arXiv  Detail & Related papers  (2022-10-21T07:25:57Z)
• Traffic Context Aware Data Augmentation for Rare Object Detection in Autonomous Driving [5.037913689432052]
  We propose a systematic study on simple Copy-Paste data augmentation for rare object detection in autonomous driving. Specifically, local adaptive instance-level image transformation is introduced to generate realistic rare object masks. We build a new dataset named NM10k consisting 10k training images, 4k validation images and the corresponding labels.
  arXiv  Detail & Related papers  (2022-05-01T01:45:00Z)
• TL-GAN: Improving Traffic Light Recognition via Data Synthesis for Autonomous Driving [8.474436072102844]
  We propose a novel traffic light generation approach TL-GAN to synthesize the data of rare classes to improve traffic light recognition for autonomous driving. In the image synthesis stage, our approach enables conditional generation to allow full control of the color of the generated traffic light images. In the sequence assembling stage, we design the style mixing and adaptive template to synthesize realistic and diverse traffic light sequences.
  arXiv  Detail & Related papers  (2022-03-28T18:12:35Z)
• An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation [65.28133251370055]
  We organize an experimental campaign with video measurement in an area within the urban network of Zurich, Switzerland. We focus on capturing the traffic state in terms of traffic flow and travel times by ensuring measurements from established thermal cameras. We propose a simple yet efficient Multiple Linear Regression (MLR) model to estimate travel times with fusion of various data sources.
  arXiv  Detail & Related papers  (2021-08-02T08:13:57Z)
• Deep Traffic Sign Detection and Recognition Without Target Domain Real Images [52.079665469286496]
  We propose a novel database generation method that requires no real image from the target-domain, and (ii) templates of the traffic signs. The method does not aim at overcoming the training with real data, but to be a compatible alternative when the real data is not available. On large data sets, training with a fully synthetic data set almost matches the performance of training with a real one.
  arXiv  Detail & Related papers  (2020-07-30T21:06:47Z)
• Deep convolutional generative adversarial networks for traffic data imputation encoding time series as images [7.053891669775769]
  We have developed a generative adversarial network (GAN) based traffic sensor data imputation framework (TGAN) In this study, we have developed a novel time-dependent encoding method called the Gramian Angular Summation Field (GASF) This study shows that the proposed model can significantly improve the traffic data imputation accuracy in terms of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) compared to state-of-the-art models on the benchmark dataset.
  arXiv  Detail & Related papers  (2020-05-05T19:14:02Z)

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.