The Vehicle Orientation Dataset

The vehicle orientation dataset contains more than one million annotations of vehicles with orientation in more than 200,000 images. It reduces the need of a secondary neural network to classify orientation by simultaneously providing both vehicle class and direction. Here is our published paper at IEEE International Conference on Big Data 2021:
Citywide reconstruction of cross-sectional traffic flow from moving camera videos»

· Download Vehicle Orientation Dataset · Download Experiment Dataset (Video and GPS)

We are organizing IEEE BigData Cup Challenge on "Vehicle class and orientation detection in the real-world using synthetic images from driving simulators." Participate to win cash prizes and free registration to attend the IEEE BigData Cup 2022 conference this year in Osaka, Japan.

• Training dataset (train-1): train-1
• Training dataset (train-2): train-2
• Test dataset (test-1): test-1

The distribution of annotations in the training dataset (train-1 and train-2) is as shown below:

We'll continue adding various object detection models trained on the vehicle orientation dataset and the synthetic vehicle orientation dataset. Open an issue if you need some specific pre-trained weights.

All vehicles in the vehicle orientation dataset are labeled with both vehicle class (five categories) and its orientation (three types).

The five classes of vehicles are:

• Car
• Bus
• Truck
• Motorcycle
• Bicycle

The three types of orientations are:

• Front
• Back
• Side

So the vehicle orientation dataset has a total of 15 classes of vehicles with orientation such car_back, car_front, car_side, bus_back, bus_front, etc.

Annotations per class in the vehicle orientation dataset follows the long-tail distribution as commonly seen in other vehicle detection data sets.

The vehicle orientation dataset is hosted on AWS S3 (Asia-pacific, Tokyo) bucket. Since the overall size of the dataset is quite big (~100GB), we have split the vehicle orientation dataset into five parts for convenience of users. Part 1 to Part 4 together contain 200,000 images (50,000 x 4) and Part 5 has 13,714 images.
Please note that the annotations are provided in YOLO format style (darknet). There is a .txt-file for each .jpg-image-file - in the same directory and with the same name. Each line contains the class and bounding box coordinates for a vehicle in the image. If there are multiple vehicles in the image, the number of lines will increase accordingly.

<object-class> <x_center> <y_center> <width> <height>

where:

• <object-class> - integer object number from 0 to (classes-1). Mapping file can be downloaded from here: Vehicle Orientation Classes
• <x_center> <y_center> <width> <height> - float values relative to width and height of image, it can be equal from (0.0 to 1.0]
• For example: <x> = <absolute_x> / <image_width> or <height> = <absolute_height> / <image_height>
• Attention: <x_center> <y_center> - are center of rectangle (Not top-left corner)

For example, for SUG007M5MX5JAZGUI4EI.jpg in vehicle-orientation-5 we have the corresponding annotation file SUG007M5MX5JAZGUI4EI.txt containing:

2 0.650000 0.573148 0.018750 0.027778
6 0.864062 0.449537 0.265625 0.793519
1 0.300000 0.581481 0.068750 0.051852
0 0.558594 0.625463 0.110937 0.217593

The first column represents the class such as car_front, car_back, etc. 2 in the first row means car_front, 6 in the second row is truck_back, and so on. Please check Vehicle Orientation Classes file for all 15 classes.

• Part-1, 50,000 images
• Part-2, 50,000 images
• Part-3, 50,000 images
• Part-4, 50,000 images
• Part-5, 13,714 images

Distributed under the MIT License. See LICENSE.txt for more information.

For any question and support, please create an issue on GitHub or write to the author here:

Ashutosh Kumar - ashutosh[at]iis.u-tokyo.ac.jp

@inproceedings{kumar2021citywide,
  title={Citywide reconstruction of cross-sectional traffic flow from moving camera videos},
  author={Kumar, Ashutosh and Kashiyama, Takehiro and Maeda, Hiroya and Sekimoto, Yoshihide},
  booktitle={2021 IEEE International Conference on Big Data (Big Data)},
  pages={1670--1678},
  year={2021},
  organization={IEEE}
}

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