Pseudo-label growth dictionary pair learning for crowd counting

Liu, Wei; Wang, Huake; Luo, Hao; Zhang, Kaibing; Lü, Jian; Xiong, Zenggang

doi:10.1007/s10489-021-02274-w

Cited by 7 publications

(3 citation statements)

References 44 publications

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“…Liu and Wang et al [65] proposed an online knowledge learning method for crowd counting. By creating a new inter-layer relationship matrix, the feature relation distillation method helps the student branch better understand how inter-layer characteristics have evolved.…”

Section: G Significant Modules In Crowd Counting Systemmentioning

confidence: 99%

“…3. Based on the review, Mean Absolute Error (MAE) and Root Mean Squared (RMSE) error were mostly used to evaluate the performance of the model whose definitions are as follows [65] :…”

Section: Performance Evaluationmentioning

confidence: 99%

“…4-6. [50] 194.1 297.8 Switch CNN [67] 318.1 439.2 LR-CNN [13] 325.6 369.4 CAT-CNN [40] 235.5 324.8 CCTrans [34] 245.0 343.6 JCTNet [54] 222.9 306.5 LibraNet [62] 181.2 262.2 MLPCNN [37] 238.63 317.28 HRANet [56] 160.9 235.8 MANet [59] 240.8 311.5 AUCNN [58] 231.8 312.4 AMSNet [42] 236.5 319.2 ASANet [51] 185.5 268.3 [65] 136.26 240.83 DEAL [50] 100.1 166.5 JCTNet [37] 90 161 TransCrowd [33] 97 168 CCTrans [34] 92 158 LibraNet [62] 88.1 143.7 MLPCNN [37] 103.61 168.9 HRANet [56] 84.6 146.2 EDENet [38] 86.6 158.5 JCCL+R [53] 100 169 AUCNN [58] 112.3 195.6 AMSNet [42] 86.5 167.2 WSITrans [55] 86.5 140. From Tables II-IV and its corresponding graph Figs.…”

Section: Performance Evaluationmentioning

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: G Significant Modules In Crowd Counting Systemmentioning

confidence: 99%

“…3. Based on the review, Mean Absolute Error (MAE) and Root Mean Squared (RMSE) error were mostly used to evaluate the performance of the model whose definitions are as follows [65] :…”

Section: Performance Evaluationmentioning

confidence: 99%