Edge computing-based real-time passenger counting using a compact convolutional neural network

Yang, Biao; Cao, Jinmeng; Liu, Xiaofeng; Wang, Nan; Lv, Jidong

doi:10.1007/s00521-018-3894-2

Cited by 5 publications

(4 citation statements)

References 28 publications

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“…Crowd counting techniques have wide range of applications ranges from pedestrian detection from UAV for crowd flow detection [11,12], passenger flow detection in exhibition center [13] and bus [14], surveillance System to detect suspicious activities, Security System, crowd analysis to avoid any disaster in public event and traffic management, military applications and health-care applications as in Fig. 2.…”

Section: Crowd Counting Applicationsmentioning

confidence: 99%

“…Clutter: An unorganized collection of items that are placed near to one another is known as clutter. It is also connected to picture noise, which makes activities like counting and recognizing more difficult [14].…”

Section: Crowd Counting Challengesmentioning

confidence: 99%

“…By adding up the pixel values from the density map calculated using a compact convolutional neural network [14], which is resistant to scale fluctuations since it uses skip connections, the counting is done. For the purpose of handling cluttered backgrounds and blurry foregrounds, a weighted Euclidean loss is suggested.…”

Section: A Deep Neural Networkmentioning

confidence: 99%

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Neural Network-Based Crowd Counting Systems: State of the Art, Challenges, and Perspectives

George,

2023

JAIT

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Crowd counting system has gained significant attention in recent years due to its relevance in various domains such as urban planning, public safety, resource allocation and decision-making in crowded environments. Due to differences in crowd densities, occlusions, size changes, and perspective distortions that are frequently seen in realworld scenarios, the system, nevertheless, falls short in terms of its purpose. To address this, it is necessary to create advanced neural network architectures, efficient methods for gathering and annotating data, reliable training procedures, and assessment criteria that accurately reflect the effectiveness of crowd counting systems. Therefore, the purpose of this study is to provide a comprehensive review of the state of the art in neural network-based crowd counting systems. The developments in neural network based crowd counting procedures, along with their features and limitations, most widely datasets and evaluation criteria are explored. The experimental findings of recent crowd counting systems are also examined. Hence, this work serves as an inspiration for additional research and development in this area, ultimately advancing crowd analysis and management systems.

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Section: Crowd Counting Applicationsmentioning

confidence: 99%

Section: Crowd Counting Challengesmentioning

confidence: 99%

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Neural Network-Based Crowd Counting Systems: State of the Art, Challenges, and Perspectives

George,

2023

JAIT

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“…50 It supports different deep learning frameworks, including TensorFlow, Caffe, and Pytorch. Besides, the TensorRT-based NVIDIA graphics processing unit, such as the high-performance embedding device Jetson TX2, 51 can be used to detect fabric defects with lightweight methods in real-time.…”

Section: Related Workmentioning

confidence: 99%

Research on textile defects detection based on improved generative adversarial network

Jin

Niu

2022

Journal of Engineered Fibers and Fabrics

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Fabric defects seriously affect the textile industry in China. Given that traditional manual detection methods have low efficiency and poor accuracy, using automatic textile defect detection methods is urgently needed. A fabric defect detection method based on an improved generative adversarial network is thus developed to address the shortage of fabric defect samples. This method learns to reconstruct the fabric image in an unsupervised manner and locates the defect areas based on the differences between the original image and the reconstruction. Afterward, the defect-related features are extracted from these areas to further recognize specific fabric defects. The central loss constraint is introduced to improve the recognition performance of this method, and lightweight processing is applied to guarantee its real-time operation in embedded systems. The application of this method is then evaluated on the publicly available Tianchi dataset. Both quantitative and qualitative results show that the proposed method can accurately detect fabric defects.

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Appearance-based passenger counting in cluttered scenes with lateral movement compensation

Sutopo

Lim

Baskaran

et al. 2021

Neural Comput & Applic

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Edge computing-based real-time passenger counting using a compact convolutional neural network

Cited by 5 publications

References 28 publications

Neural Network-Based Crowd Counting Systems: State of the Art, Challenges, and Perspectives

Neural Network-Based Crowd Counting Systems: State of the Art, Challenges, and Perspectives

Research on textile defects detection based on improved generative adversarial network

Appearance-based passenger counting in cluttered scenes with lateral movement compensation

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