BDD100K: A Diverse Driving Video Database with Scalable Annotation Tooling
Explore this paper's citation graph
Summary
The design and implementation of a scalable annotation system that can provide a comprehensive set of image labels for large-scale driving datasets, and a new driving dataset, which is an order of magnitude larger than previous efforts.
- Type
- preprint
- Published
- 2018-05-12
- Cited by
- 970
- References
- 26
- Access
- Open access
- OpenAlex
- https://openalex.org/W2799352588
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:44138353
Keywords
Annotation, Computer science, Scalability, Bounding overwatch, Segmentation
References
- UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop
- Efficiently Scaling up Crowdsourced Video Annotation
- ActivityNet: A large-scale video benchmark for human activity understanding
- A practical system for road marking detection and recognition
- Pedestrian detection: A benchmark
- ImageNet: A large-scale hierarchical image database
- LabelMe: A Database and Web-Based Tool for Image Annotation
- Vision meets robotics: The KITTI dataset
- Real time detection of lane markers in urban streets
- Learning Deep Features for Scene Recognition using Places Database
- A new performance measure and evaluation benchmark for road detection algorithms
- The Cityscapes Dataset for Semantic Urban Scene Understanding
- YouTube-8M: A Large-Scale Video Classification Benchmark
- 1 year, 1000 km: The Oxford RobotCar dataset
- End-to-End Learning of Driving Models from Large-Scale Video Datasets
- CityPersons: A Diverse Dataset for Pedestrian Detection
- Annotating Object Instances with a Polygon-RNN
- Dilated Residual Networks
Cited by
- The ApolloScape Dataset for Autonomous Driving
- Learning Driving Models with a Surround-View Camera System and a Route Planner
- A Systematic Comparison of Deep Learning Architectures in an Autonomous Vehicle
- CFENet: An Accurate and Efficient Single-Shot Object Detector for Autonomous Driving
- Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks
- Interest point detectors stability evaluation on ApolloScape dataset
- The ApolloScape Open Dataset for Autonomous Driving and Its Application
- DLRAD – A FIRST LOOK ON THE NEW VISION AND MAPPING BENCHMARK DATASET FOR AUTONOMOUS DRIVING
- Seeding Deep Learning using Wireless Localization
- The UAVid Dataset for Video Semantic Segmentation
- VeData: Promoting AI Assisted Autonomous Vehicles
- Addressing Training Bias via Automated Image Annotation
- Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data
- Automatic Annotation of Object Instances by Region-Based Recurrent Neural Networks
- Large-Scale Visual Active Learning with Deep Probabilistic Ensembles
- Adaptive Semantic Segmentation with a Strategic Curriculum of Proxy Labels
- A 64-Line Lidar-Based Road Obstacle Sensing Algorithm for Intelligent Vehicles
- IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments
- Diverse Image Synthesis From Semantic Layouts via Conditional IMLE
- Towards Accurate Task Accomplishment with Low-Cost Robotic Arms
Related papers
- Mask R-CNN
- YOLOv3: An Incremental Improvement
- The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes
- Pyramid Scene Parsing Network
- The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes
- DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs