A method for people counting using feature fusion based on SVR with PSO optimization

Yuan, Jiaojiao; Lou, Haitao; Bao, Hong; Xu, Cheng

doi:10.1109/iccsn.2017.8230347

Cited by 2 publications

(1 citation statement)

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“…For low-density crowds, statistical data from pixels and feature points can depict changes in highly dense crowds. The authors in this study fuse the pixels and corners, while SVR is used to learn the corresponding connection between the feature and the number of persons [32]. In 2010, Felzenszwalb et al [7] proposed a system based on a latent support vector machine (SVM) framework, which incorporates a mixture of parts with multiscale deformations within the model.…”

Section: Local Approachesmentioning

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: Local Approachesmentioning

confidence: 99%