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LISA Traffic Light Dataset

4301614367
Tagself-driving
Taskobject detection
Release YearMade in 2016
LicenseCC BY-NC-SA 4.0
Download5 GB

Introduction #

Jensen Morten Born, Philipsen Mark Philip, Mogelmose Andreaset al.

To provide a shared basis for comparing traffic light recognition (TLR) systems, the authors publish an extensive public LISA Traffic Light Dataset based on footage from US roads. The dataset contains annotated video sequences, captured under varying light and weather conditions using a stereo camera. The database consists of continuous test and training video sequences, totaling 43,007 frames and 113,888 annotated traffic lights. The sequences are captured by a stereo camera mounted on the roof of a vehicle driving under both night- and daytime with varying light and weather conditions.

Motivation

The effectiveness of transportation systems profoundly influences workforce mobility, environmental conditions, and energy consumption, thereby exerting a significant impact on foreign policy. Given the integral role transportation plays in people’s daily lives, its efficiency, safety, and cleanliness directly affect their health and well-being. Future enhancements to transportation systems are anticipated to stem from advancements in sensing, communication, and processing technologies. The advent of the automobile revolution in the early 20th century sparked a dramatic surge in road transportation, overwhelming the capacity of existing road networks to accommodate the escalating traffic volume. In response, traffic control devices (TCD) were developed to facilitate efficient and safe transportation by guiding, regulating, and warning drivers. These TCDs encompass various infrastructure elements, including signs, signaling lights, and pavement markings, aimed at communicating critical information to drivers.

image

Traffic control devices for safe and efficient traffic flow.

Traffic control devices (TCDs) play a crucial role in complex environments like intersections, where a wealth of information needs to be conveyed. Balancing the provision of adequate information with the avoidance of overwhelming and distracting drivers is key. The effectiveness of TCDs hinges on the driver’s ability to process the information within the constraints of time and volume. Excessive speed and information overload can lead to errors and stress among drivers.

For TCDs to function optimally, compliance from all road users is essential to prevent potentially hazardous situations. However, there are instances where drivers deliberately ignore TCDs. Research indicates that more than a third of Americans admit to intentionally running red lights in the past month. Non-compliance can stem from various factors such as rushing to beat a light, aggressive driving behaviors, distractions, misunderstandings, or faulty TCDs. While driving is often perceived as effortless due to automation of many tasks, this can lead to drivers being less focused, resulting in delayed reactions to critical events. Conversely, highly attentive driving can also lead to delayed reaction times due to stress, fatigue, or mental overload.

While widespread adoption of autonomous driving remains a distant prospect, lives can be safeguarded through the implementation of driver assistance systems (DAS) capable of monitoring the environment and intervening in critical situations. To effectively support drivers, DAS must compensate for their limitations. For instance, drivers may have difficulty noticing and recognizing certain TCDs. Studies indicate that while speed limit signs are almost always noticed, pedestrian crossing signs are often overlooked. The reaction times of drivers is longest in the center of the interval, where the decision is the most difficult.

image

Fused DAS system in intersection scenarios. (a) Turn right on red assistance. (b) Dilemma zone assistance.

Traffic lights

Traffic lights (TLs) play a vital role in regulating traffic flow by providing clear instructions to drivers regarding the right of way. This allocation of right of way is meticulously designed to minimize conflicts between vehicles and pedestrians traversing intersecting paths. TLs are intentionally conspicuous, employing bright-colored lamps, typically circular or arrow-shaped, housed within uniformly colored containers. The standard TL configuration features the familiar red-yellow-green sequence, with each light indicating whether drivers should halt, prepare to stop, or proceed. However, to address the complexities of various intersections, a range of alternative TL configurations has been developed.

image

Examples of vertical TLs found in California.

The orientation, color, size, and shape of the container will vary country to country and even city to city.

image

(a) San Diego, California. (b) Cincinnati, Ohio.

Besides the various configurations of TLs, the state sequence is an important characteristic of a TL. For increasing road safety and making it easier for drivers when driving across states, TLs in USA are regulated by the Federal Highway Administration in the Manual on Uniform Traffic Control Devices.

image

Basic TL sequence for states: green, yellow, and red.

Dataset description

Until now no comprehensive survey of traffic light recognition (TLR) research has been published. The authors presented an introductory overview of ongoing work on traffic light detection along with the LISA Traffic Light Dataset. Most published TLR systems are evaluated on datasets which are unavailable to the public. This makes comparison between existing methods and new contributions difficult. The contributions made in this survey paper are thus, fourfold:

  1. Clarifying challenges facing TLR systems.
  2. Overview of current methods in TLR research.
  3. Common evaluation procedure for TLR systems.
  4. High resolution, annotated, stereo video dataset.

The LISA Traffic Light Dataset comprises traffic lights (TLs) located in San Diego, California, USA. This dataset offers two daytime and two nighttime sequences for testing purposes. The test sequences entail 23 minutes and 25 seconds of driving through San Diego. Stereo image pairs are captured using Point Grey’s Bumblebee XB3 (BBX3-13S2C-60), equipped with three lenses, each capturing images at a resolution of 1280x960. These lenses boast a Field of View (FoV) of 66°. With three lenses, the stereo camera accommodates two different baselines, 12 and 24 cm, with the wider baseline utilized for the LISA Traffic Light Dataset. The stereo images are uncompressed and rectified in real time. The Bumblebee XB3 is positioned centrally on the roof of the vehicle and connected to a laptop via FireWire-800 (IEEE-1394b). In addition to the four test sequences, 18 shorter video clips are provided for training and testing purposes. Manual adjustments were made to the gain and shutter speed to prevent oversaturation and minimize flickering effects from the TLs. For daytime clips, the shutter speed was set to 1/5000 sec with a gain of 0, while for nighttime clips, the shutter speed was 1/100 sec with a gain of 8. Alongside the stereo images, a Triclops calibration file is included, containing the factory calibration data for the Bumblebee XB3 camera used in the dataset.

The LISA Traffic Light Dataset utilizes stereo vision capturing techniques, as stereo vision is widely employed in various computer vision applications, including Traffic Light Recognition (TLR). Each sequence within the dataset is accompanied by manually labeled annotations specifically for the left stereo frame. These annotations encompass essential details such as the frame number, the outlined rectangular area surrounding the illuminated traffic light (TL) lamp, and its corresponding state. Examining a heatmap generated from all annotations within the dataset reveals a consistent trend: the majority of annotations cluster in the upper right portion of the frames, with only a few TLs annotated on the far left side. Consequently, it is prudent to focus the search for traffic lights primarily on the upper regions of the frames.

image

Aspect ratio histogram of LISA TL Dataset.

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Dataset LinkHomepageDataset LinkResearch Paper

Summary #

LISA Traffic Light Dataset is a dataset for an object detection task. It is used in the automotive industry.

The dataset consists of 43016 images with 219832 labeled objects belonging to 14 different classes including go, stop, go traffic light, and other: stop traffic light, stop left, stop left traffic light, go left, go left traffic light, warning, warning traffic light, warning left, warning left traffic light, go forward, and go forward traffic light.

Images in the LISA Traffic Light dataset have bounding box annotations. There are 6626 (15% of the total) unlabeled images (i.e. without annotations). There are 2 splits in the dataset: test (22481 images) and train (20535 images). Alternatively, the dataset could be split into 2 times of day: day (24988 images) and night (18028 images). Additionally, every image marked with its sequence and track frame number tags. The dataset was released in 2016 by the BEUMER Group, Denmark, Aalborg University, Denmark, and University of California, USA.

Dataset Poster

Explore #

LISA Traffic Light dataset has 43016 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

OpenSample annotation mask from LISA Traffic LightSample image from LISA Traffic Light
OpenSample annotation mask from LISA Traffic LightSample image from LISA Traffic Light
OpenSample annotation mask from LISA Traffic LightSample image from LISA Traffic Light
OpenSample annotation mask from LISA Traffic LightSample image from LISA Traffic Light
OpenSample annotation mask from LISA Traffic LightSample image from LISA Traffic Light
OpenSample annotation mask from LISA Traffic LightSample image from LISA Traffic Light
OpenSample annotation mask from LISA Traffic LightSample image from LISA Traffic Light
OpenSample annotation mask from LISA Traffic LightSample image from LISA Traffic Light
OpenSample annotation mask from LISA Traffic LightSample image from LISA Traffic Light
OpenSample annotation mask from LISA Traffic LightSample image from LISA Traffic Light
OpenSample annotation mask from LISA Traffic LightSample image from LISA Traffic Light
OpenSample annotation mask from LISA Traffic LightSample image from LISA Traffic Light
OpenSample annotation mask from LISA Traffic LightSample image from LISA Traffic Light
OpenSample annotation mask from LISA Traffic LightSample image from LISA Traffic Light
OpenSample annotation mask from LISA Traffic LightSample image from LISA Traffic Light
OpenSample annotation mask from LISA Traffic LightSample image from LISA Traffic Light
OpenSample annotation mask from LISA Traffic LightSample image from LISA Traffic Light
OpenSample annotation mask from LISA Traffic LightSample image from LISA Traffic Light
OpenSample annotation mask from LISA Traffic LightSample image from LISA Traffic Light
OpenSample annotation mask from LISA Traffic LightSample image from LISA Traffic Light
OpenSample annotation mask from LISA Traffic LightSample image from LISA Traffic Light
OpenSample annotation mask from LISA Traffic LightSample image from LISA Traffic Light
OpenSample annotation mask from LISA Traffic LightSample image from LISA Traffic Light
OpenSample annotation mask from LISA Traffic LightSample image from LISA Traffic Light
OpenSample annotation mask from LISA Traffic LightSample image from LISA Traffic Light
OpenSample annotation mask from LISA Traffic LightSample image from LISA Traffic Light
OpenSample annotation mask from LISA Traffic LightSample image from LISA Traffic Light
OpenSample annotation mask from LISA Traffic LightSample image from LISA Traffic Light
OpenSample annotation mask from LISA Traffic LightSample image from LISA Traffic Light
OpenSample annotation mask from LISA Traffic LightSample image from LISA Traffic Light
👀
Have a look at 43016 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 14 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Search
Rows 1-10 of 14
Class
Images
Objects
Count on image
average
Area on image
average
go
rectangle
18678
47237
2.53
0.11%
stop
rectangle
18652
44574
2.39
0.12%
go traffic light
rectangle
18626
46723
2.51
0.26%
stop traffic light
rectangle
18591
44318
2.38
0.26%
stop left
rectangle
10380
12750
1.23
0.05%
stop left traffic light
rectangle
10367
12734
1.23
0.25%
go left
rectangle
1888
2490
1.32
0.1%
go left traffic light
rectangle
1874
2476
1.32
0.26%
warning
rectangle
1129
2750
2.44
0.09%
warning traffic light
rectangle
1106
2669
2.41
0.16%

Co-occurrence matrix #

Co-occurrence matrix is an extremely valuable tool that shows you the images for every pair of classes: how many images have objects of both classes at the same time. If you click any cell, you will see those images. We added the tooltip with an explanation for every cell for your convenience, just hover the mouse over a cell to preview the description.

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Object distribution #

Interactive heatmap chart for every class with object distribution shows how many images are in the dataset with a certain number of objects of a specific class. Users can click cell and see the list of all corresponding images.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Search
Rows 1-10 of 14
Class
Object count
Avg area
Max area
Min area
Min height
Min height
Max height
Max height
Avg height
Avg height
Min width
Min width
Max width
Max width
go
rectangle
47237
0.04%
1.55%
0%
3px
0.31%
171px
17.81%
23px
2.4%
3px
0.23%
161px
12.58%
go traffic light
rectangle
46723
0.1%
1.92%
0.01%
13px
1.35%
195px
20.31%
41px
4.24%
8px
0.62%
161px
12.58%
stop
rectangle
44574
0.05%
0.45%
0%
3px
0.31%
100px
10.42%
27px
2.84%
3px
0.23%
61px
4.77%
stop traffic light
rectangle
44318
0.11%
0.62%
0.01%
11px
1.15%
113px
11.77%
43px
4.52%
7px
0.55%
70px
5.47%
stop left
rectangle
12750
0.04%
1.78%
0%
4px
0.42%
185px
19.27%
22px
2.33%
4px
0.31%
118px
9.22%
stop left traffic light
rectangle
12734
0.2%
1.78%
0.01%
11px
1.15%
185px
19.27%
53px
5.53%
7px
0.55%
118px
9.22%
warning
rectangle
2750
0.04%
0.55%
0%
3px
0.31%
101px
10.52%
23px
2.38%
3px
0.23%
71px
5.55%
warning traffic light
rectangle
2669
0.07%
0.55%
0.01%
11px
1.15%
101px
10.52%
35px
3.67%
7px
0.55%
71px
5.55%
go left
rectangle
2490
0.08%
1.39%
0%
4px
0.42%
151px
15.73%
30px
3.14%
4px
0.31%
161px
12.58%
go left traffic light
rectangle
2476
0.2%
1.69%
0.02%
16px
1.67%
191px
19.9%
55px
5.7%
10px
0.78%
161px
12.58%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Spatial Heatmap

Objects #

Table contains all 219832 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Search
Rows 1-10 of 100848
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
go traffic light
rectangle
dayClip2--00718.jpg
960 x 1280
66px
6.88%
43px
3.36%
0.23%
2
go
rectangle
dayClip2--00718.jpg
960 x 1280
14px
1.46%
15px
1.17%
0.02%
3
go traffic light
rectangle
nightClip2--01048.jpg
960 x 1280
46px
4.79%
28px
2.19%
0.1%
4
go traffic light
rectangle
nightClip2--01048.jpg
960 x 1280
34px
3.54%
29px
2.27%
0.08%
5
go
rectangle
nightClip2--01048.jpg
960 x 1280
16px
1.67%
16px
1.25%
0.02%
6
go
rectangle
nightClip2--01048.jpg
960 x 1280
12px
1.25%
17px
1.33%
0.02%
7
go traffic light
rectangle
dayClip12--00083.jpg
960 x 1280
56px
5.83%
43px
3.36%
0.2%
8
go traffic light
rectangle
dayClip12--00083.jpg
960 x 1280
76px
7.92%
61px
4.77%
0.38%
9
go
rectangle
dayClip12--00083.jpg
960 x 1280
12px
1.25%
15px
1.17%
0.01%
10
go
rectangle
dayClip12--00083.jpg
960 x 1280
16px
1.67%
21px
1.64%
0.03%

License #

LISA Traffic Light Dataset is under CC BY-NC-SA 4.0 license.

Source

Citation #

If you make use of the LISA Traffic Light data, please cite the following reference:

@article{jensen2016vision,
  title={Vision for looking at traffic lights: Issues, survey, and perspectives},
  author={Jensen, Morten Born{\o} and Philipsen, Mark Philip and M{\o}gelmose, Andreas and Moeslund, Thomas Baltzer and Trivedi, Mohan Manubhai},
  journal={IEEE Transactions on Intelligent Transportation Systems},
  volume={17},
  number={7},
  pages={1800--1815},
  year={2016},
  doi={10.1109/TITS.2015.2509509},
  publisher={IEEE}
}

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-lisa-traffic-light-dataset,
  title = { Visualization Tools for LISA Traffic Light Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/lisa-traffic-light } },
  url = { https://datasetninja.com/lisa-traffic-light },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { apr },
  note = { visited on 2024-04-14 },
}

Download #

Dataset LISA Traffic Light can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='LISA Traffic Light', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here.

. . .

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