BDD100K

The tasks are based on BDD100K, the largest driving video dataset to date supporting heterogenous multi-task learning. It contains 100,000 videos representing more than 1000 hours of driving experience with more than 100 million frames. The videos comes with GPU/IMU data for trajectory information. The BDD100K dataset now provide annotations of the 10 tasks: image tagging, lane detection, drivable area segmentation, object detection, semantic segmentation, instance segmentation, multi-object detection tracking, multi-object segmentation tracking, domain adaptation and imitation learning. These diverse tasks make the study of heterogenous multi-task learning possible. For the CVPR 2020 Workshop on Autonomous Driving, we host the multi-object detection tracking challenge on CodaLab detailed below. Challenges on the other tasks will be announced on our dataset website. Video Data Explore 100,000 HD video sequences of over 1,100-hour driving experience across many different times in the day, weather conditions, and driving scenarios. Our video sequences also include GPS locations, IMU data, and timestamps. Road Object Detection 2D Bounding Boxes annotated on 100,000 images for bus, traffic light, traffic sign, person, bike, truck, motor, car, train, and rider. Instance Segmentation Explore over 10,000 diverse images with pixel-level and rich instance-level annotations. Driveable Area Learn complicated drivable decision from 100,000 images. Lane Markings Multiple types of lane marking annotations on 100,000 images for driving guidance.

本数据集的任务基于BDD100K——这是目前支持异构多任务学习的规模最大的驾驶视频数据集。该数据集包含10万条驾驶视频，累计时长超1000小时，总帧数超过1亿，所有视频均附带用于轨迹信息采集的图形处理器（GPU）与惯性测量单元（IMU）数据。
BDD100K数据集目前提供10类任务的标注：图像标注、车道线检测、可行驶区域分割、目标检测、语义分割、实例分割、多目标检测跟踪、多目标分割跟踪、域自适应以及模仿学习。这些多样化的任务为异构多任务学习的研究提供了可行场景。
在2020年国际计算机视觉与模式识别会议（CVPR 2020）自动驾驶专题研讨会上，我们于CodaLab平台举办了多目标检测跟踪挑战赛，详情如下。其余任务的挑战赛信息将在本数据集官方网站发布。
视频数据：探索横跨不同时段、天气条件与驾驶场景的10万条高清视频序列，累计时长超1100小时。本数据集的视频序列还包含全球定位系统（GPS）位置信息、IMU数据与时间戳。
道路目标检测：针对巴士、交通信号灯、交通标志、行人、自行车、卡车、摩托车、轿车、火车与骑行者，在10万张图像上标注了2D边界框。
实例分割：包含超过1万张涵盖像素级与丰富实例级标注的多样化图像。
可行驶区域：从10万张图像中学习复杂的可行驶区域决策逻辑。
车道标线：在10万张图像上标注了多种类型的车道标线，用于辅助驾驶决策。

搜集汇总

背景与挑战

背景概述

BDD100K是目前最大的驾驶视频数据集，包含10万个高清视频，总时长超过1,100小时，涵盖多种时间、天气和驾驶场景。它支持10项异构多任务学习，包括目标检测、语义分割和实例分割等，并附带GPS和IMU数据，主要用于自动驾驶和计算机视觉研究。