Crowd Detection and Analysis for Surveillance Videos using Deep Learning

Ahmed, Aman; Bansal, Prateek; Khan, Atiya; Purohit, Neha

doi:10.1109/icesc51422.2021.9532683

Cited by 14 publications

(1 citation statement)

References 23 publications

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“…the execution of populace density gauges in tram-trains, where MH-MetroNet performs better compared to the latest cutting-edge designs where openly accessible source code is accessible. In 2021, Ahmed et al [38] proposed a video detection framework for crowd detection. By counting distinct individuals and then conducting crowd analysis, the presence of a crowd is discovered.…”

Section: Advanced Methodsmentioning

confidence: 99%

Deep Learning Based Efficient Crowd Counting System

Al-Ghanem,

Qazi,

Faheem

et al. 2024

CMC

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Estimation of crowd count is becoming crucial nowadays, as it can help in security surveillance, crowd monitoring, and management for different events. It is challenging to determine the approximate crowd size from an image of the crowd's density. Therefore in this research study, we proposed a multi-headed convolutional neural network architecture-based model for crowd counting, where we divided our proposed model into two main components: (i) the convolutional neural network, which extracts the feature across the whole image that is given to it as an input, and (ii) the multi-headed layers, which make it easier to evaluate density maps to estimate the number of people in the input image and determine their number in the crowd. We employed the available public benchmark crowd-counting datasets UCF CC 50 and ShanghaiTech parts A and B for model training and testing to validate the model's performance. To analyze the results, we used two metrics Mean Absolute Error (MAE) and Mean Square Error (MSE), and compared the results of the proposed systems with the state-of-art models of crowd counting. The results show the superiority of the proposed system.

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Section: Advanced Methodsmentioning

confidence: 99%