# Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting

arXiv: https://arxiv.org/abs/2003.05586 · HTML: https://mlsanity.com/paper/2003.05586/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2020.

## Built on by (most cited first, 22 of 22)

- [Revisiting Crowd Counting: State-of-the-art, Trends, and Future Perspectives](https://mlsanity.com/paper/2209.07271/) (2022)
- [FusionCount: Efficient Crowd Counting via Multiscale Feature Fusion](https://mlsanity.com/paper/2202.13660/) (2022)
- [CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification](https://mlsanity.com/paper/2403.09281/) (2024)
- [Backdoor Attacks on Crowd Counting](https://mlsanity.com/paper/2207.05641/) (2022)
- [Counting Varying Density Crowds Through Density Guided Adaptive Selection CNN and Transformer Estimation](https://mlsanity.com/paper/2206.10075/) (2022)
- [Learning Discriminative Features for Crowd Counting](https://mlsanity.com/paper/2311.04509/) (2023)
- [LCDnet: A Lightweight Crowd Density Estimation Model for Real-time Video Surveillance](https://mlsanity.com/paper/2302.05374/) (2023)
- [SoybeanNet: Transformer-Based Convolutional Neural Network for Soybean Pod Counting from Unmanned Aerial Vehicle (UAV) Images](https://mlsanity.com/paper/2310.10861/) (2023)
- [VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results](https://mlsanity.com/paper/2107.08766/) (2021)
- [Towards More Effective PRM-based Crowd Counting via A Multi-resolution Fusion and Attention Network](https://mlsanity.com/paper/2112.09664/) (2021)
- [Multimodal Crowd Counting with Pix2Pix GANs](https://mlsanity.com/paper/2401.07591/) (2024)
- [Region-Aware Network: Model Human's Top-Down Visual Perception Mechanism for Crowd Counting](https://mlsanity.com/paper/2106.12163/) (2021)
- [PANet: Perspective-Aware Network with Dynamic Receptive Fields and Self-Distilling Supervision for Crowd Counting](https://mlsanity.com/paper/2111.00406/) (2021)
- [Reducing Capacity Gap in Knowledge Distillation with Review Mechanism for Crowd Counting](https://mlsanity.com/paper/2206.05475/) (2022)
- [Inception-Based Crowd Counting -- Being Fast while Remaining Accurate](https://mlsanity.com/paper/2210.09796/) (2022)
- [Scale-Aware Crowd Counting Using a Joint Likelihood Density Map and Synthetic Fusion Pyramid Network](https://mlsanity.com/paper/2211.06835/) (2022)
- [Tolerating Annotation Displacement in Dense Object Counting via Point Annotation Probability Map](https://mlsanity.com/paper/2308.00530/) (2023)
- [Scale-Aware Crowd Count Network with Annotation Error Correction](https://mlsanity.com/paper/2312.16771/) (2023)
- [A Recipe for CAC: Mosaic-based Generalized Loss for Improved Class-Agnostic Counting](https://mlsanity.com/paper/2404.09826/) (2024)
- [ANTHROPOS-V: benchmarking the novel task of Crowd Volume Estimation](https://mlsanity.com/paper/2501.01877/) (2025)
- [RepSFNet : A Single Fusion Network with Structural Reparameterization for Crowd Counting](https://mlsanity.com/paper/2601.20369/) (2026)
- [FSCA-Net: Feature-Separated Cross-Attention Network for Robust Multi-Dataset Training](https://mlsanity.com/paper/2602.01540/) (2026)

## Abstract (arXiv)

In this paper, we propose two modified neural networks based on dual path multi-scale fusion networks (SFANet) and SegNet for accurate and efficient crowd counting. Inspired by SFANet, the first model, which is named M-SFANet, is attached with atrous spatial pyramid pooling (ASPP) and context-aware module (CAN). The encoder of M-SFANet is enhanced with ASPP containing parallel atrous convolutional layers with different sampling rates and hence able to extract multi-scale features of the target object and incorporate larger context. To further deal with scale variation throughout an input image, we leverage the CAN module which adaptively encodes the scales of the contextual information. The combination yields an effective model for counting in both dense and sparse crowd scenes. Based on the SFANet decoder structure, M-SFANet's decoder has dual paths, for density map and attention map generation. The second model is called M-SegNet, which is produced by replacing the bilinear upsampling in SFANet with max unpooling that is used in SegNet. This change provides a faster model while providing competitive counting performance. Designed for high-speed surveillance applications, M-SegNet has no additional multi-scale-aware module in order to not increase the complexity. Both models are encoder-decoder based architectures and are end-to-end trainable. We conduct extensive experiments on five crowd counting datasets and one vehicle counting dataset to show that these modifications yield algorithms that could improve state-of-the-art crowd counting methods. Codes are available at https://github.com/Pongpisit-Thanasutives/Variations-of-SFANet-for-Crowd-Counting.