Cross-Line Pedestrian Counting Based on Spatially-Consistent Two-Stage Local Crowd Density Estimation and Accumulation

Zheng, Huicheng; Lin, Zijian; Cen, Jiepeng; Wu, Zeyu; Zhao, Yu–Qiu

doi:10.1109/tcsvt.2018.2807806

Cited by 26 publications

(7 citation statements)

References 68 publications

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“…In this subsection, we briefly introduce some special crowd counting datasets, which are only used in some certain scenarios. These datasets contain line crowd counting (LHI [137], [142], crowd sequences (PETS [138], Venice [86]), multi-sources (AHU-Crowd [139], [143], CI-ISR [149], Venice [86]), indoor (MICC [140], Indoor 1 [141], Indoor 2 [150]), train station (TS [144], STF [144]), subway station (Shanghai Subway Station [145]), BRT (Beijing BRT [146] 13 ), bridge (EBP [147]), airport (ZhengzhouAirport [151]), categorized [152]. The specific statistics of these datasets are listed in Tabel III.…”

Section: Some Special Crowd Counting Datasetsmentioning

confidence: 99%

CNN-based Density Estimation and Crowd Counting: A Survey

Gao,

Liu

et al. 2020

Preprint

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Section: Some Special Crowd Counting Datasetsmentioning

confidence: 99%

CNN-based Density Estimation and Crowd Counting: A Survey

Gao,

Liu

et al. 2020

Preprint

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“…Regression-based methods establish the correspondences between the input image and the number of people. Conventional methods [ 20 , 21 , 22 , 23 , 24 ] use carefully designed handcrafted features and apply different regression methods to regress the final count number. Although they achieved progress, their performances are constrained due to the handcrafted features of their methods, which heavily rely on the specific crowd scenes.…”

Section: Related Workmentioning

confidence: 99%

Congested Crowd Counting via Adaptive Multi-Scale Context Learning

Zhang

Zhao

Duan

et al. 2021

Sensors

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In this paper, we propose a novel congested crowd counting network for crowd density estimation, i.e., the Adaptive Multi-scale Context Aggregation Network (MSCANet). MSCANet efficiently leverages the spatial context information to accomplish crowd density estimation in a complicated crowd scene. To achieve this, a multi-scale context learning block, called the Multi-scale Context Aggregation module (MSCA), is proposed to first extract different scale information and then adaptively aggregate it to capture the full scale of the crowd. Employing multiple MSCAs in a cascaded manner, the MSCANet can deeply utilize the spatial context information and modulate preliminary features into more distinguishing and scale-sensitive features, which are finally applied to a 1 × 1 convolution operation to obtain the crowd density results. Extensive experiments on three challenging crowd counting benchmarks showed that our model yielded compelling performance against the other state-of-the-art methods. To thoroughly prove the generality of MSCANet, we extend our method to two relevant tasks: crowd localization and remote sensing object counting. The extension experiment results also confirmed the effectiveness of MSCANet.

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“…Although crowds comprise of separate individuals, everyone has their own aims and behavioral patterns. The nature of the crowds and their distinct features are commonly interpreted to get cooperative characteristics that can be commonly defined [2,3]. Crowd data like density and flow are vital factors in handling, designing, and managing public places such as political gatherings, temples, etc.…”

Section: Introductionmentioning

confidence: 99%

Sparrow Search Optimization with Transfer Learning-Based Crowd Density Classification

Yamin¹,

Almutairi²,

Badghish³

et al. 2023

Computers, Materials &Amp; Continua

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Due to the rapid increase in urbanization and population, crowd gatherings are frequently observed in the form of concerts, political, and religious meetings. HAJJ is one of the well-known crowding events that takes place every year in Makkah, Saudi Arabia. Crowd density estimation and crowd monitoring are significant research areas in Artificial Intelligence (AI) applications. The current research study develops a new Sparrow Search Optimization with Deep Transfer Learning based Crowd Density Detection and Classification (SSODTL-CD2C) model. The presented SSODTL-CD2C technique majorly focuses on the identification and classification of crowd densities. To attain this, SSODTL-CD2C technique exploits Oppositional Salp Swarm Optimization Algorithm (OSSA) with EfficientNet model to derive the feature vectors. At the same time, Stacked Sparse Auto Encoder (SSAE) model is utilized for the classification of crowd densities. Finally, SSO algorithm is employed for optimal fine-tuning of the parameters involved in SSAE mechanism. The performance of the proposed SSODTL-CD2C technique was validated using a dataset with four different kinds of crowd densities. The obtained results demonstrated that the proposed SSODTL-CD2C methodology accomplished an excellent crowd classification performance with a maximum accuracy of 93.25%. So, the proposed method will be highly helpful in managing HAJJ and other crowded events.

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Cross-Line Pedestrian Counting Based on Spatially-Consistent Two-Stage Local Crowd Density Estimation and Accumulation

Cited by 26 publications

References 68 publications

CNN-based Density Estimation and Crowd Counting: A Survey

CNN-based Density Estimation and Crowd Counting: A Survey

Congested Crowd Counting via Adaptive Multi-Scale Context Learning

Sparrow Search Optimization with Transfer Learning-Based Crowd Density Classification

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