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Multiscale Aggregate Networks with Dense Connections for Crowd Counting.

Pengfei Li¹, Min Zhang¹, Jian Wan¹

¹Hangzhou Dianzi University, Baiyang Road No. 2, Hangzhou, China.

Computational Intelligence and Neuroscience

|November 22, 2021

Summary

This summary is machine-generated.

This study introduces the Multiscale Aggregation Network (MANet) to improve crowd counting accuracy by addressing feature loss. MANet enhances density map generation for precise crowd number estimation.

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Area of Science:

Computer Vision
Deep Learning
Artificial Intelligence

Background:

Crowd counting methods using fully convolutional networks face challenges with multiscale and contextual information loss.
Accurate crowd density map generation is crucial for reliable crowd counting.

Purpose of the Study:

To propose and evaluate a novel Multiscale Aggregation Network (MANet) for enhanced crowd counting.
To overcome the limitations of existing methods in handling multiscale and contextual information.

Main Methods:

Developed a Multiscale Aggregation Network (MANet) comprising a feature extraction encoder (FEE) and a density map decoder (DMD).
The FEE employs a cascaded scale pyramid network for multiscale feature extraction and dense connections for contextual information.
The DMD utilizes deconvolution and fusion operations to generate detailed features for high-quality density maps.

Main Results:

MANet demonstrated superior performance compared to existing methods on four benchmark datasets (ShanghaiTech, WorldExpo'10, UCF_CC_50, and SmartCity).
The proposed method achieved lower mean absolute error (MAE) and mean squared error (MSE), indicating higher accuracy in crowd counting.

Conclusions:

The Multiscale Aggregation Network (MANet) effectively addresses multiscale and contextual loss in crowd counting.
MANet provides a more accurate and robust solution for estimating crowd density and counting individuals.