Joint Attention for Autonomous Driving
JAAD is a new dataset for studying joint attention in the context of autonomous driving. In particular, the focus is on pedestrian and driver behaviors at the point of crossing and factors that influence them. To this end, JAAD dataset provides an annotated collection of short video clips representing scenes typical for everyday urban driving in various weather conditions.
JAAD dataset contains 346 high-resolution video clips (most are 5-10 sec) extracted from approx. 240 hours of driving videos filmed in several locations in North America and Eastern Europe.
Our dataset contains 88K frames with 2793 unique pedestrians labeled with over 390K bounding boxes. Occlusion tags are provided for each bounding box. ~55K (13%) of bounding boxes are tagged with partial occlusion and ~49K (12%) with heavy occlusion.
Behavioral data and attributes are provided for 868 pedestrians.
Note that statistics provided here may differ from the numbers in published papers since the dataset has been updated several times after the paper submission deadlines.
There are two types of annotations provided for the dataset: textual and bounding boxes.
Textual annotations contain descriptions of behaviors for those pedestrians and cars that interact with or require attention of the driver.
For each video there are several tags (weather, location, whether it is a designated crossing, time of the day, age and gender of the pedestrians, etc.), 3 types of subjects (driver, car, pedestrian) and timestamped behavior descriptions from a fixed list (e.g. stopped, moving fast, walking, looking, signalling, etc).
Behavioral annotations are created using BORIS - an event logging software for video observations.
In addition, we provide a list of attributes for each pedestrian (e.g. age, gender, direction of motion, etc.) and a list of visible traffic scene elements (e.g. stop sign, traffic signal, etc.) for each video.
Follow the links below to download JAAD dataset and find more information about the available annotations.