Aerial-Ground Intelligent Unmanned System Environment Perception Challenge
Aerial-Ground Intelligent Unmanned System Environment Perception Challenge
Task: Object Detection
Task: Object Detection

In object detection task, we focus on ten object categories of interest including pedestrian, person, car, van, bus, truck, motor, bicycle, awning-tricycle, and tricycle. Some rarely occurring special vehicles.

Task: Crowd Counting
Task: Crowd Counting

The challenge will provide 2720 pairs of images, including 1808 pairs of images for training, and 912 pairs of images for testing.

Task: Visual SLAM
Task: Visual SLAM

This visual SLAM benchmark is based on the FusionPortable dataset, which has been collected by covering a variety of environments on The Hong Kong University of Science and Technology.

Smart device (drones and robots), equipped with embedded sensing devices have been fast deployed to a wide range of applications, including agricultural, aerial photography, fast delivery, motion obstacle avoidance, and industrial automation. Consequently, automatic understanding of visual data collected from these platforms become highly demanding, which brings computer vision to smart device more and more closely. We are excited to present a large-scale benchmark with carefully annotated ground-truth for various important computer vision tasks, named AGEP 2022 .

The VisDrone2022 dataset is collected by the AISKYEYE team at Lab of Machine Learning and Data Mining , Tianjin University, China. The benchmark dataset consists of 400 video clips formed by 265,228 frames and 10,209 static images, captured by various drone-mounted cameras, covering a wide range of aspects including location (taken from 14 different cities separated by thousands of kilometers in China), environment (urban and country), objects (pedestrian, vehicles, bicycles, etc.), and density (sparse and crowded scenes). Note that, the dataset was collected using various drone platforms (i.e., drones with different models), in different scenarios, and under various weather and lighting conditions. These frames are manually annotated with more than 2.6 million bounding boxes or points of targets of frequent interests, such as pedestrians, cars, bicycles, and tricycles. Some important attributes including scene visibility, object class and occlusion, are also provided for better data utilization.

This FusionPortable-VSLAM Challenge 2022 is based on the FusionPortable dataset, which has been collected by covering a variety of environments on The Hong Kong University of Science and Technology. campus by exploiting multiple platforms for data collection. It provides a large range of difficult problems for SLAM. All these sequences are characterized by structure-less areas and varying illumination conditions that are typical in real-world scenarios and pose great challenges to SLAM algorithms that have been developed in confined lab environments. Accurate ground truth, at centimeter-level, is provided for each sequence. The sensor platform used to record the data includes 10Hz LiDAR point clouds, 20Hz stereo frame images, high-rate and asynchronous events from stereo event cameras, 200Hz acceleration and angular velocity readings from an IMU, and 10Hz GPS signal outdoors.

News

  • AGEP 2022 will be organized in conjunction with PRCV 2022.
  • ICCV2021 Workshop: Vision Meets Drones 2021: A Challenge Zoom link.
  • Paper submission system is available now .The deadline for workshop paper is August 7 2021, AOE time.
  • The deadline for the competition is 24:00 on July 15th 2021, AOE time
  • VisDrone 2021 will be organized in conjunction with ICCV 2021.
  • Aug. 28, 2020: Computer Vision for UAVs Workshop and Challenge will be held at 8:00 (UTC+1) on August 28.
  • July. 14, 2020: Evaluation server will be closed at 23:59 on July 15 (UTC+0 time).
  • July. 9, 2020: Paper submission system is available now. Paper submission deadline is delayed until July 15th. 
  • June. 26, 2020: Due to the impact of COVID-19, the submission deadline is delayed until July 15th. Each team will have additional 5 submission opportunities.

Citation

Zhu P, Wen L, Du D, et al. Detection and Tracking Meet Drones Challenge[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2021 (01): 1-1. Bibtex source | Abstract | PDF