# Crowd counting with segmentation attention convolutional neural network

arXiv: https://arxiv.org/abs/2204.07380 · HTML: https://mlsanity.com/paper/2204.07380/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 47 references that are in the map.
- First posted on arXiv 15 Apr 2022.

## Builds on (most cited first, 19 of 19)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Learning from Synthetic Data for Crowd Counting in the Wild](https://mlsanity.com/paper/1903.03303/) (2019)
- [Switching Convolutional Neural Network for Crowd Counting](https://mlsanity.com/paper/1708.00199/) (2017)
- [Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs](https://mlsanity.com/paper/1708.00953/) (2017)
- [CrowdNet: A Deep Convolutional Network for Dense Crowd Counting](https://mlsanity.com/paper/1608.06197/) (2016)
- [DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation](https://mlsanity.com/paper/1712.06679/) (2017)
- [CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting](https://mlsanity.com/paper/1707.09605/) (2017)
- [A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density Estimation](https://mlsanity.com/paper/1707.01202/) (2017)
- [People, Penguins and Petri Dishes: Adapting Object Counting Models To New Visual Domains And Object Types Without Forgetting](https://mlsanity.com/paper/1711.05586/) (2017)
- [Crowded Scene Analysis: A Survey](https://mlsanity.com/paper/1502.01812/) (2015)
- [Crowd Counting Using Scale-Aware Attention Networks](https://mlsanity.com/paper/1903.02025/) (2019)
- [Divide and Grow: Capturing Huge Diversity in Crowd Images with Incrementally Growing CNN](https://mlsanity.com/paper/1807.09993/) (2018)
- [Top-Down Feedback for Crowd Counting Convolutional Neural Network](https://mlsanity.com/paper/1807.08881/) (2018)
- [Ten Years of Pedestrian Detection, What Have We Learned?](https://mlsanity.com/paper/1411.4304/) (2014)
- [Scene Invariant Crowd Segmentation and Counting Using Scale-Normalized Histogram of Moving Gradients (HoMG)](https://mlsanity.com/paper/1602.00386/) (2016)

## Abstract (arXiv)

Deep learning occupies an undisputed dominance in crowd counting. In this paper, we propose a novel convolutional neural network (CNN) architecture called SegCrowdNet. Despite the complex background in crowd scenes, the proposeSegCrowdNet still adaptively highlights the human head region and suppresses the non-head region by segmentation. With the guidance of an attention mechanism, the proposed SegCrowdNet pays more attention to the human head region and automatically encodes the highly refined density map. The crowd count can be obtained by integrating the density map. To adapt the variation of crowd counts, SegCrowdNet intelligently classifies the crowd count of each image into several groups. In addition, the multi-scale features are learned and extracted in the proposed SegCrowdNet to overcome the scale variations of the crowd. To verify the effectiveness of our proposed method, extensive experiments are conducted on four challenging datasets. The results demonstrate that our proposed SegCrowdNet achieves excellent performance compared with the state-of-the-art methods.