# Detection, Tracking, and Counting Meets Drones in Crowds: A Benchmark

arXiv: https://arxiv.org/abs/2105.02440 · HTML: https://mlsanity.com/paper/2105.02440/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 45 references that are in the map.
- First posted on arXiv 6 May 2021.

## Builds on (most cited first, 26 of 26)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://mlsanity.com/paper/1612.01925/) (2016)
- [PointConv: Deep Convolutional Networks on 3D Point Clouds](https://mlsanity.com/paper/1811.07246/) (2018)
- [CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes](https://mlsanity.com/paper/1802.10062/) (2018)
- [Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds](https://mlsanity.com/paper/1808.01050/) (2018)
- [Drone-based Object Counting by Spatially Regularized Regional Proposal Network](https://mlsanity.com/paper/1707.05972/) (2017)
- [NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization](https://mlsanity.com/paper/2001.03360/) (2020)
- [Context-Aware Crowd Counting](https://mlsanity.com/paper/1811.10452/) (2018)
- [Distribution Matching for Crowd Counting](https://mlsanity.com/paper/2009.13077/) (2020)
- [Single-Shot Refinement Neural Network for Object Detection](https://mlsanity.com/paper/1711.06897/) (2017)
- [Switching Convolutional Neural Network for Crowd Counting](https://mlsanity.com/paper/1708.00199/) (2017)
- [Where are the Blobs: Counting by Localization with Point Supervision](https://mlsanity.com/paper/1807.09856/) (2018)
- [CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting](https://mlsanity.com/paper/1707.09605/) (2017)
- [Locality-constrained Spatial Transformer Network for Video Crowd Counting](https://mlsanity.com/paper/1907.07911/) (2019)
- [Spatiotemporal Modeling for Crowd Counting in Videos](https://mlsanity.com/paper/1707.07890/) (2017)
- [FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras](https://mlsanity.com/paper/1707.09476/) (2017)
- [Multi-scale Convolutional Neural Networks for Crowd Counting](https://mlsanity.com/paper/1702.02359/) (2017)
- [Crowd counting via scale-adaptive convolutional neural network](https://mlsanity.com/paper/1711.04433/) (2017)
- [Hybrid Graph Neural Networks for Crowd Counting](https://mlsanity.com/paper/2002.00092/) (2020)
- [Estimating People Flows to Better Count Them in Crowded Scenes](https://mlsanity.com/paper/1911.10782/) (2019)
- [An Aggregated Multicolumn Dilated Convolution Network for Perspective-Free Counting](https://mlsanity.com/paper/1804.07821/) (2018)
- [Stacked Pooling: Improving Crowd Counting by Boosting Scale Invariance](https://mlsanity.com/paper/1808.07456/) (2018)

## Built on by (most cited first, 30 of 31)

- [An End-to-End Transformer Model for Crowd Localization](https://mlsanity.com/paper/2202.13065/) (2022)
- [CrowdCLIP: Unsupervised Crowd Counting via Vision-Language Model](https://mlsanity.com/paper/2304.04231/) (2023)
- [UrbanVideo-Bench: Benchmarking Vision-Language Models on Embodied Intelligence with Video Data in Urban Spaces](https://mlsanity.com/paper/2503.06157/) (2025)
- [The Caltech Fish Counting Dataset: A Benchmark for Multiple-Object Tracking and Counting](https://mlsanity.com/paper/2207.09295/) (2022)
- [The State of Aerial Surveillance: A Survey](https://mlsanity.com/paper/2201.03080/) (2022)
- [Multiview Aerial Visual Recognition (MAVREC): Can Multi-view Improve Aerial Visual Perception?](https://mlsanity.com/paper/2312.04548/) (2023)
- [RingMo-Aerial: An Aerial Remote Sensing Foundation Model With Affine Transformation Contrastive Learning](https://mlsanity.com/paper/2409.13366/) (2024)
- [NWPU-MOC: A Benchmark for Fine-grained Multi-category Object Counting in Aerial Images](https://mlsanity.com/paper/2401.10530/) (2024)
- [Open-World Object Counting in Videos](https://mlsanity.com/paper/2506.15368/) (2025)
- [Backdoor Attacks on Crowd Counting](https://mlsanity.com/paper/2207.05641/) (2022)
- [RGB-D Indiscernible Object Counting in Underwater Scenes](https://mlsanity.com/paper/2304.11677/) (2023)
- [Oriented object detection in optical remote sensing images using deep learning: a survey](https://mlsanity.com/paper/2302.10473/) (2023)
- [FGENet: Fine-Grained Extraction Network for Congested Crowd Counting](https://mlsanity.com/paper/2401.01208/) (2024)
- [Open-World Drone Active Tracking with Goal-Centered Rewards](https://mlsanity.com/paper/2412.00744/) (2024)
- [Single Domain Generalization for Few-Shot Counting via Universal Representation Matching](https://mlsanity.com/paper/2505.16778/) (2025)
- [Video Individual Counting for Moving Drones](https://mlsanity.com/paper/2503.10701/) (2025)
- [Instance-level Visual Active Tracking with Occlusion-Aware Planning](https://mlsanity.com/paper/2604.21453/) (2026)
- [RefCrowd: Grounding the Target in Crowd with Referring Expressions](https://mlsanity.com/paper/2206.08172/) (2022)
- [RIS-LAD: A Benchmark and Model for Referring Low-Altitude Drone Image Segmentation](https://mlsanity.com/paper/2507.20920/) (2025)
- [Video Individual Counting and Tracking from Moving Drones: A Benchmark and Methods](https://mlsanity.com/paper/2601.12500/) (2026)
- [SENTRY: SAM2-Enhanced Neighbor-Aware and Temporally Reasoned Memory for Visual Tracking](https://mlsanity.com/paper/2606.24449/) (2026)
- [Self-Aligning Depth-regularized Radiance Fields for Asynchronous RGB-D Sequences](https://mlsanity.com/paper/2211.07459/) (2022)
- [Cattle Detection Occlusion Problem](https://mlsanity.com/paper/2212.11418/) (2022)
- [Density-based clustering with fully-convolutional networks for crowd flow detection from drones](https://mlsanity.com/paper/2301.04937/) (2023)
- [Improving trajectory continuity in drone-based crowd monitoring using a set of minimal-cost techniques and deep discriminative correlation filters](https://mlsanity.com/paper/2504.20234/) (2025)
- [One-Shot Crowd Counting With Density Guidance For Scene Adaptation](https://mlsanity.com/paper/2602.07955/) (2026)
- [A High-Level Survey of Optical Remote Sensing](https://mlsanity.com/paper/2602.17397/) (2026)
- [Count Anything](https://mlsanity.com/paper/2605.30846/) (2026)
- [UAV-OVVIS: Unmanned Aerial Vehicles Also Need Open-Vocabulary Video Instance Segmentation](https://mlsanity.com/paper/2607.08075/) (2026)
- [Object Counting Across Modalities: Taxonomies, Benchmarks, Applications, and Open Challenges](https://mlsanity.com/paper/2608.23845/) (2026)

## Abstract (arXiv)

To promote the developments of object detection, tracking and counting algorithms in drone-captured videos, we construct a benchmark with a new drone-captured largescale dataset, named as DroneCrowd, formed by 112 video clips with 33,600 HD frames in various scenarios. Notably, we annotate 20,800 people trajectories with 4.8 million heads and several video-level attributes. Meanwhile, we design the Space-Time Neighbor-Aware Network (STNNet) as a strong baseline to solve object detection, tracking and counting jointly in dense crowds. STNNet is formed by the feature extraction module, followed by the density map estimation heads, and localization and association subnets. To exploit the context information of neighboring objects, we design the neighboring context loss to guide the association subnet training, which enforces consistent relative position of nearby objects in temporal domain. Extensive experiments on our DroneCrowd dataset demonstrate that STNNet performs favorably against the state-of-the-arts.