TrafficCAM: A Versatile Dataset for Traffic Flow Segmentation

• URL: http://arxiv.org/abs/2211.09620v1
• Date: Thu, 17 Nov 2022 16:14:38 GMT
• Title: TrafficCAM: A Versatile Dataset for Traffic Flow Segmentation
• Authors: Zhongying Deng, Yanqi Chen, Lihao Liu, Shujun Wang, Rihuan Ke, Carola-Bibiane Schonlieb, Angelica I Aviles-Rivero
• Abstract summary: Existing traffic flow datasets have two major limitations. They feature a limited number of classes, usually limited to one type of vehicle, and the scarcity of unlabelled data. We introduce a new benchmark traffic flow image dataset called TrafficCAM.
• Score: 9.744937939618161
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traffic flow analysis is revolutionising traffic management. Qualifying traffic flow data, traffic control bureaus could provide drivers with real-time alerts, advising the fastest routes and therefore optimising transportation logistics and reducing congestion. The existing traffic flow datasets have two major limitations. They feature a limited number of classes, usually limited to one type of vehicle, and the scarcity of unlabelled data. In this paper, we introduce a new benchmark traffic flow image dataset called TrafficCAM. Our dataset distinguishes itself by two major highlights. Firstly, TrafficCAM provides both pixel-level and instance-level semantic labelling along with a large range of types of vehicles and pedestrians. It is composed of a large and diverse set of video sequences recorded in streets from eight Indian cities with stationary cameras. Secondly, TrafficCAM aims to establish a new benchmark for developing fully-supervised tasks, and importantly, semi-supervised learning techniques. It is the first dataset that provides a vast amount of unlabelled data, helping to better capture traffic flow qualification under a low cost annotation requirement. More precisely, our dataset has 4,402 image frames with semantic and instance annotations along with 59,944 unlabelled image frames. We validate our new dataset through a large and comprehensive range of experiments on several state-of-the-art approaches under four different settings: fully-supervised semantic and instance segmentation, and semi-supervised semantic and instance segmentation tasks. Our benchmark dataset will be released.

Related papers

• Cross-domain Few-shot In-context Learning for Enhancing Traffic Sign Recognition [49.20086587208214]
  We propose a cross-domain few-shot in-context learning method based on the MLLM for enhancing traffic sign recognition. By using description texts, our method reduces the cross-domain differences between template and real traffic signs. Our approach requires only simple and uniform textual indications, without the need for large-scale traffic sign images and labels.
  arXiv  Detail & Related papers  (2024-07-08T10:51:03Z)
• eTraM: Event-based Traffic Monitoring Dataset [23.978331129798356]
  We present eTraM, a first-of-its-kind, fully event-based traffic monitoring dataset. eTraM offers 10 hr of data from different traffic scenarios in various lighting and weather conditions. It covers eight distinct classes of traffic participants, ranging from vehicles to pedestrians and micro-mobility.
  arXiv  Detail & Related papers  (2024-03-29T04:58:56Z)
• 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)
• Lens: A Foundation Model for Network Traffic [19.3652490585798]
  Lens is a foundation model for network traffic that leverages the T5 architecture to learn the pre-trained representations from large-scale unlabeled data. We design a novel loss that combines three distinct tasks: Masked Span Prediction (MSP), Packet Order Prediction (POP), and Homologous Traffic Prediction (HTP)
  arXiv  Detail & Related papers  (2024-02-06T02:45:13Z)
• FDTI: Fine-grained Deep Traffic Inference with Roadnet-enriched Graph [10.675666104503119]
  We propose Fine-grained Deep Traffic Inference, as termedI. We construct a fine-grained traffic graph based on traffic signals to model the inter-road relations. We are the first to conduct the city-level fine-grained traffic prediction.
  arXiv  Detail & Related papers  (2023-06-19T14:03:42Z)
• OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping [84.65114565766596]
  We present OpenLane-V2, the first dataset on topology reasoning for traffic scene structure. OpenLane-V2 consists of 2,000 annotated road scenes that describe traffic elements and their correlation to the lanes. We evaluate various state-of-the-art methods, and present their quantitative and qualitative results on OpenLane-V2 to indicate future avenues for investigating topology reasoning in traffic scenes.
  arXiv  Detail & Related papers  (2023-04-20T16:31:22Z)
• 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)
• Multi-task Learning for Sparse Traffic Forecasting [13.359590890052454]
  We propose a multi-task learning network that can simultaneously predict the congestion classes and the speed of each road segment. Our method achieved excellent results on the dataset provided by the Traffic4cast Competition 2022, source code is available at https://github.com/OctopusLi/NeurIPS2022-traffic4cast.
  arXiv  Detail & Related papers  (2022-11-18T02:10:40Z)
• Road Network Guided Fine-Grained Urban Traffic Flow Inference [108.64631590347352]
  Accurate inference of fine-grained traffic flow from coarse-grained one is an emerging yet crucial problem. We propose a novel Road-Aware Traffic Flow Magnifier (RATFM) that exploits the prior knowledge of road networks. Our method can generate high-quality fine-grained traffic flow maps.
  arXiv  Detail & Related papers  (2021-09-29T07:51:49Z)
• Deep traffic light detection by overlaying synthetic context on arbitrary natural images [49.592798832978296]
  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.
  arXiv  Detail & Related papers  (2020-11-07T19:57:22Z)

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.