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Related Experiment Video

Updated: Sep 30, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

COMAL: compositional multi-scale feature enhanced learning for crowd counting.

Fangbo Zhou¹, Huailin Zhao¹, Yani Zhang²

¹School of Electrical and Electronic Engineering, Shanghai Institute of Technology, Shanghai, China.

Multimedia Tools and Applications

|March 16, 2022

Summary

This summary is machine-generated.

This study introduces a new method to improve crowd counting accuracy by better modeling head scale variations. The compositional multi-scale feature enhanced learning approach (COMAL) enhances feature representation for more reliable crowd density estimation.

Keywords:

Convolutional neural network Crowd counting Crowd density estimation Multi-scale feature learning

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Last Updated: Sep 30, 2025

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Published on: December 15, 2023

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Published on: January 13, 2023

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Published on: May 7, 2019

Area of Science:

Computer Vision
Artificial Intelligence

Background:

Crowd counting methods often struggle with extreme scale variations and complex scenes.
Existing multi-branch networks have limited scale representability and context awareness.

Purpose of the Study:

To propose a compositional multi-scale feature enhanced learning approach (COMAL) for improved crowd counting.
To address limitations in scale representability, feature diversity, and contextual understanding in crowd counting.

Main Methods:

Developed the compositional multi-scale feature enhanced learning approach (COMAL).
Introduced Semantic Enhanced Module (SEM), Diversity Enhanced Module (DEM), and Context Enhanced Module (CEM) within COMAL.
Implemented COMAL within an encoder-decoder framework for crowd counting.

Main Results:

COMAL effectively enhances multi-scale feature representations for crowd counting.
Experiments on ShanghaiTech, UCF_CC_50, and UCF-QNRF datasets show significant improvements.
The method demonstrates effectiveness in handling extreme scale variations and complex scenes.

Conclusions:

The proposed COMAL approach significantly improves crowd counting accuracy.
COMAL's modules enhance semantic, diversity, and contextual information for better feature representation.
This work offers a more robust solution for crowd density estimation in challenging scenarios.