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Updated: Oct 22, 2025

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

Deep Learning-Based Crowd Scene Analysis Survey.

Sherif Elbishlawi¹, Mohamed H Abdelpakey², Agwad Eltantawy¹

¹The University of British Columbia, 3333 University Way, Kelowna, BC V1V 1V7, Canada.

Journal of Imaging

|August 30, 2021

Summary

This summary is machine-generated.

This paper reviews deep learning methods for automatic crowd scene analysis, covering crowd counting and action recognition. It also introduces a new metric to evaluate crowd counting accuracy in videos.

Keywords:

crowd action recognition crowd counting crowd scene deep learning

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Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Recent global events highlight the need for automatic crowd scene analysis for management, security, and tracking.
Analyzing crowded scenes presents significant challenges, including occlusion, complex behaviors, and posture variations.

Purpose of the Study:

To survey deep learning-based methods for crowd scene analysis.
To categorize reviewed methods into crowd counting and crowd action recognition.
To propose a novel evaluation metric for crowd scene analysis.

Main Methods:

Systematic review of deep learning techniques applied to crowd counting and action recognition.
Survey of existing crowd scene datasets.
Development of a new metric to quantify the accuracy of crowd counting in video sequences.

Main Results:

Categorization of deep learning methods into crowd counting and action recognition.
Overview of relevant crowd scene datasets.
Introduction of a proposed evaluation metric for crowd counting performance.

Conclusions:

Deep learning offers promising solutions for complex crowd scene analysis.
Standardized evaluation metrics are crucial for advancing the field of crowd analysis.
Further research is needed to address challenges like occlusion and complex behaviors in dense crowds.