mlsanityData to 7 Oct 2026

VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results

arXiv 2107.08766 ↗First posted 19 Jul 2021cs.CVcited by 5

Not on the map. Why?

Abstract (arXiv)

Crowd counting on the drone platform is an interesting topic in computer vision, which brings new challenges such as small object inference, background clutter and wide viewpoint. However, there are few algorithms focusing on crowd counting on the drone-captured data due to the lack of comprehensive datasets. To this end, we collect a large-scale dataset and organize the Vision Meets Drone Crowd Counting Challenge (VisDrone-CC2020) in conjunction with the 16th European Conference on Computer Vision (ECCV 2020) to promote the developments in the related fields. The collected dataset is formed by 3,360 images, including 2,460 images for training, and 900 images for testing. Specifically, we manually annotate persons with points in each video frame. There are 14 algorithms from 15 institutes submitted to the VisDrone-CC2020 Challenge. We provide a detailed analysis of the evaluation results and conclude the challenge. More information can be found at the website: urlhttp://www.aiskyeye.com/.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 25 of its 46 references that are in the map. First posted on arXiv 19 Jul 2021.

Builds on

25 of 46 references.

References · 25

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  6. 2018Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowdscited by 186
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  10. 2019Bi-Directional Cascade Network for Perceptual Edge Detectioncited by 71
  11. 2019Crowd Counting and Density Estimation by Trellis Encoder-Decoder Networkcited by 56
  12. 2019Perspective-Guided Convolution Networks for Crowd Countingcited by 54
13 more
  1. 2019Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rankcited by 52
  2. 2018ADCrowdNet: An Attention-injective Deformable Convolutional Network for Crowd Understandingcited by 45
  3. 2020CNN-based Density Estimation and Crowd Counting: A Surveycited by 43
  4. 2019AutoScale: Learning to Scale for Crowd Counting and Localizationcited by 35
  5. 2019Counting with Focus for Freecited by 33
  6. 2019Learn to Scale: Generating Multipolar Normalized Density Maps for Crowd Countingcited by 32
  7. 2019Locality-constrained Spatial Transformer Network for Video Crowd Countingcited by 30
  8. 2017Crowd counting via scale-adaptive convolutional neural networkcited by 23
  9. 2020Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Countingcited by 22
  10. 2019Dual Path Multi-Scale Fusion Networks with Attention for Crowd Countingcited by 12
  11. 2019MRCNet: Crowd Counting and Density Map Estimation in Aerial and Ground Imagerycited by 10
  12. 2020Rethinking Object Detection in Retail Storescited by 9
  13. 2019Drone-based Joint Density Map Estimation, Localization and Tracking with Space-Time Multi-Scale Attention Networkcited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 0; 2025: 0; 2026: 1.

5 papers.

  1. 2022An End-to-End Transformer Model for Crowd Localizationcited by 74
  2. 2021Panoptic Segmentation: A Reviewcited by 18
  3. 2022Spatio-channel Attention Blocks for Cross-modal Crowd Countingcited by 11
  4. 2026GAAT: Geometry-Aware Alignment Transformer for Multimodal UAV Perceptioncited by 1
  5. 2023Density-based clustering with fully-convolutional networks for crowd flow detection from dronescited by 0