Traffic Scene Parsing through the TSP6K Dataset

• URL: http://arxiv.org/abs/2303.02835v2
• Date: Sat, 30 Mar 2024 01:21:22 GMT
• Title: Traffic Scene Parsing through the TSP6K Dataset
• Authors: Peng-Tao Jiang, Yuqi Yang, Yang Cao, Qibin Hou, Ming-Ming Cheng, Chunhua Shen,
• Abstract summary: 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.
• Score: 109.69836680564616
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Traffic scene perception in computer vision is a critically important task to achieve intelligent cities. To date, most existing datasets focus on autonomous driving scenes. We observe that the models trained on those driving datasets often yield unsatisfactory results on traffic monitoring scenes. However, little effort has been put into improving the traffic monitoring scene understanding, mainly due to the lack of specific datasets. To fill this gap, we introduce a specialized traffic monitoring dataset, termed TSP6K, containing images from the traffic monitoring scenario, with high-quality pixel-level and instance-level annotations. The TSP6K dataset captures more crowded traffic scenes with several times more traffic participants than the existing driving scenes. We perform a detailed analysis of the dataset and comprehensively evaluate previous popular scene parsing methods, instance segmentation methods and unsupervised domain adaption methods. Furthermore, considering the vast difference in instance sizes, we propose a detail refining decoder for scene parsing, which recovers the details of different semantic regions in traffic scenes owing to the proposed TSP6K dataset. Experiments show its effectiveness in parsing the traffic monitoring scenes. Code and dataset are available at https://github.com/PengtaoJiang/TSP6K.

Related papers

• 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)
• Traffic Reconstruction and Analysis of Natural Driving Behaviors at Unsignalized Intersections [1.7273380623090846]
  This research involved recording traffic at various unsignalized intersections in Memphis, TN, during different times of the day. After manually labeling video data to capture specific variables, we reconstructed traffic scenarios in the SUMO simulation environment. The output data from these simulations offered a comprehensive analysis, including time-space diagrams for vehicle movement, travel time frequency distributions, and speed-position plots to identify bottleneck points.
  arXiv  Detail & Related papers  (2023-12-22T09:38:06Z)
• TrafficMOT: A Challenging Dataset for Multi-Object Tracking in Complex Traffic Scenarios [23.831048188389026]
  Multi-object tracking in traffic videos offers immense potential for enhancing traffic monitoring accuracy and promoting road safety measures. Existing datasets for multi-object tracking in traffic videos often feature limited instances or focus on single classes. We introduce TrafficMOT, an extensive dataset designed to encompass diverse traffic situations with complex scenarios.
  arXiv  Detail & Related papers  (2023-11-30T18:59:56Z)
• 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)
• TrafficCAM: A Versatile Dataset for Traffic Flow Segmentation [9.744937939618161]
  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.
  arXiv  Detail & Related papers  (2022-11-17T16:14:38Z)
• Monocular BEV Perception of Road Scenes via Front-to-Top View Projection [57.19891435386843]
  We present a novel framework that reconstructs a local map formed by road layout and vehicle occupancy in the bird's-eye view. Our model runs at 25 FPS on a single GPU, which is efficient and applicable for real-time panorama HD map reconstruction.
  arXiv  Detail & Related papers  (2022-11-15T13:52:41Z)
• Vision-Language Navigation with Random Environmental Mixup [112.94609558723518]
  Vision-language Navigation (VLN) tasks require an agent to navigate step-by-step while perceiving the visual observations and comprehending a natural language instruction. Previous works have proposed various data augmentation methods to reduce data bias. We propose the Random Environmental Mixup (REM) method, which generates cross-connected house scenes as augmented data via mixuping environment.
  arXiv  Detail & Related papers  (2021-06-15T04:34:26Z)
• SceneGen: Learning to Generate Realistic Traffic Scenes [92.98412203941912]
  We present SceneGen, a neural autoregressive model of traffic scenes that eschews the need for rules and distributions. We demonstrate SceneGen's ability to faithfully model distributions of real traffic scenes.
  arXiv  Detail & Related papers  (2021-01-16T22:51:43Z)
• 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)
• Towards Accurate Vehicle Behaviour Classification With Multi-Relational Graph Convolutional Networks [22.022759283770377]
  We propose a pipeline for understanding vehicle behaviour from a monocular image sequence or video. A temporal sequence of such encodings is fed to a recurrent network to label vehicle behaviours. The proposed framework can classify a variety of vehicle behaviours to high fidelity on datasets that are diverse.
  arXiv  Detail & Related papers  (2020-02-03T14:34:28Z)

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.