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KITTI Road Object Detection

Autonomous driving benchmark for object detection (Geiger et al., CVPR 2012). Annotate vehicles, pedestrians, and cyclists with 3D bounding boxes and occlusion/truncation labels.

Labels:outdoornatureurbanpeopleanimal+−

Configuration Fileconfig.yaml

This Potato config reproduces the annotation task. Save it as config.yaml and run potato start config.yaml to try it.

yaml
# KITTI Road Object Detection Configuration
# Based on Geiger et al., CVPR 2012

annotation_task_name: "KITTI Object Detection"
task_dir: "."

data_files:
  - "sample-data.json"

item_properties:
  id_key: "id"
  text_key: "image_url"
  context_key: "context"

user_config:
  allow_all_users: true

annotation_schemes:
  - annotation_type: "multiselect"
    name: "object_types"
    description: "Select all object types visible"
    labels:
      - name: "car"
        tooltip: "Cars, vans"
      - name: "van"
        tooltip: "Vans, minivans"
      - name: "truck"
        tooltip: "Trucks"
      - name: "pedestrian"
        tooltip: "People walking"
      - name: "person_sitting"
        tooltip: "Person sitting"
      - name: "cyclist"
        tooltip: "Person on bicycle"
      - name: "tram"
        tooltip: "Trams, streetcars"
      - name: "misc"
        tooltip: "Other objects"
      - name: "dontcare"
        tooltip: "Regions to ignore"

  - annotation_type: "radio"
    name: "occlusion_level"
    description: "Occlusion level of primary object"
    labels:
      - name: "fully_visible"
        tooltip: "0: Fully visible"
      - name: "partly_occluded"
        tooltip: "1: Partly occluded"
      - name: "largely_occluded"
        tooltip: "2: Largely occluded"
      - name: "unknown"
        tooltip: "3: Unknown"

  - annotation_type: "radio"
    name: "truncation"
    description: "Truncation level (object leaving image)"
    labels:
      - name: "not_truncated"
        tooltip: "Object fully within image"
      - name: "truncated"
        tooltip: "Object partially outside image"

  - annotation_type: "radio"
    name: "difficulty"
    description: "Detection difficulty"
    labels:
      - name: "easy"
        tooltip: "Min height 40px, fully visible"
      - name: "moderate"
        tooltip: "Min height 25px, partly occluded"
      - name: "hard"
        tooltip: "Min height 25px, difficult to see"

  - annotation_type: "text"
    name: "bbox_coordinates"
    description: "Bounding box: left, top, right, bottom (pixels)"

interface_config:
  item_display_format: "<img src='{{text}}' style='max-width:100%; max-height:500px;'/><br/><small>{{context}}</small>"

output_annotation_format: "json"
output_annotation_dir: "annotations"

Sample Datasample-data.json

json
[
  {
    "id": "kitti_001",
    "image_url": "https://upload.wikimedia.org/wikipedia/commons/thumb/7/75/Great_Portland_Street_London_Traffic.jpg/1280px-Great_Portland_Street_London_Traffic.jpg",
    "context": "Road scene from driving perspective. Annotate all vehicles, pedestrians, and cyclists with bounding boxes."
  },
  {
    "id": "kitti_002",
    "image_url": "https://upload.wikimedia.org/wikipedia/commons/thumb/3/37/Rome_Traffic.jpg/1280px-Rome_Traffic.jpg",
    "context": "Urban driving scene. Mark occlusion and truncation levels for each object."
  }
]

// ... and 1 more items

Get This Design

View on GitHub

Clone or download from the repository

Quick start:

git clone https://github.com/davidjurgens/potato-showcase.git
cd potato-showcase/image/driving/kitti
potato start config.yaml

Dataset & paper

Geiger et al., CVPR 2012

Official dataset ↗Read the paper ↗

Citation (BibTeX)

bibtex
@inproceedings{geiger2012we,
    title = "Are we ready for Autonomous Driving? {The KITTI} Vision Benchmark Suite",
    author = "Geiger, Andreas  and Lenz, Philip  and Urtasun, Raquel",
    booktitle = "2012 IEEE Conference on Computer Vision and Pattern Recognition",
    pages = "3354--3361",
    year = "2012",
    organization = "IEEE",
    doi = "10.1109/CVPR.2012.6248074",
    url = "https://ieeexplore.ieee.org/document/6248074"
}

Details

Annotation Types

multiselectradiotext

Domain

Computer VisionAutonomous Driving

Use Cases

Object Detection3D DetectionAutonomous Vehicles

Tags

kittiautonomous-driving3d-detectionvehiclescvpr2012

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