Salma Saud Alghamdi;Lama Al Khuzayem;Ohoud Alzamzami
{"title":"基于视觉的半监督学习时间序列人群预测","authors":"Salma Saud Alghamdi;Lama Al Khuzayem;Ohoud Alzamzami","doi":"10.1109/ACCESS.2025.3604713","DOIUrl":null,"url":null,"abstract":"Crowd forecasting is a crucial component of public safety, urban planning, and event management, enabling proactive decision-making based on anticipated crowd dynamics. Traditional sensor-based approaches, such as WiFi-based methods, suffer from accuracy issues due to device penetration limitations. On the other hand, vision-based approaches, while more precise, typically require fully extensive labeled data and high computational resources. These demands restrict their application to forecasting often limited to predicting the next frame or a few seconds ahead. To overcome these challenges, this research presents a vision-based time series forecasting framework that exploits a semi-supervised deep learning approach. A semi-supervised crowd counting model, trained on just 5% of labeled images from a single day, is used to extract time series crowd counts from images captured over 16 days at 5-minute intervals. These extracted time series data are then used for training multiple Long Short-Term Memory (LSTM) variants to analyze the dynamics of crowd forecasting. Experimental results demonstrate that the proposed framework enables accurate crowd forecasting while reducing annotation costs. Unlike existing vision-based approaches, which are constrained to forecasting seconds ahead, our approach can forecast a horizon of one hour ahead. Notably, the CNN Autoencoder LSTM and ConvLSTM models achieved an RMSE of 61.93 and a MAPE of 26.13%. These findings highlight the effectiveness of semi-supervised learning with minimal labeled data in vision-based crowd forecasting. Future work will focus on improving generalizability and robustness across different urban environments.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"153523-153541"},"PeriodicalIF":3.6000,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11145448","citationCount":"0","resultStr":"{\"title\":\"Vision-Based Time Series Crowd Forecasting Using Semi-Supervised Learning\",\"authors\":\"Salma Saud Alghamdi;Lama Al Khuzayem;Ohoud Alzamzami\",\"doi\":\"10.1109/ACCESS.2025.3604713\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Crowd forecasting is a crucial component of public safety, urban planning, and event management, enabling proactive decision-making based on anticipated crowd dynamics. Traditional sensor-based approaches, such as WiFi-based methods, suffer from accuracy issues due to device penetration limitations. On the other hand, vision-based approaches, while more precise, typically require fully extensive labeled data and high computational resources. These demands restrict their application to forecasting often limited to predicting the next frame or a few seconds ahead. To overcome these challenges, this research presents a vision-based time series forecasting framework that exploits a semi-supervised deep learning approach. A semi-supervised crowd counting model, trained on just 5% of labeled images from a single day, is used to extract time series crowd counts from images captured over 16 days at 5-minute intervals. These extracted time series data are then used for training multiple Long Short-Term Memory (LSTM) variants to analyze the dynamics of crowd forecasting. Experimental results demonstrate that the proposed framework enables accurate crowd forecasting while reducing annotation costs. Unlike existing vision-based approaches, which are constrained to forecasting seconds ahead, our approach can forecast a horizon of one hour ahead. Notably, the CNN Autoencoder LSTM and ConvLSTM models achieved an RMSE of 61.93 and a MAPE of 26.13%. These findings highlight the effectiveness of semi-supervised learning with minimal labeled data in vision-based crowd forecasting. Future work will focus on improving generalizability and robustness across different urban environments.\",\"PeriodicalId\":13079,\"journal\":{\"name\":\"IEEE Access\",\"volume\":\"13 \",\"pages\":\"153523-153541\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2025-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11145448\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Access\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11145448/\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Access","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/11145448/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Vision-Based Time Series Crowd Forecasting Using Semi-Supervised Learning
Crowd forecasting is a crucial component of public safety, urban planning, and event management, enabling proactive decision-making based on anticipated crowd dynamics. Traditional sensor-based approaches, such as WiFi-based methods, suffer from accuracy issues due to device penetration limitations. On the other hand, vision-based approaches, while more precise, typically require fully extensive labeled data and high computational resources. These demands restrict their application to forecasting often limited to predicting the next frame or a few seconds ahead. To overcome these challenges, this research presents a vision-based time series forecasting framework that exploits a semi-supervised deep learning approach. A semi-supervised crowd counting model, trained on just 5% of labeled images from a single day, is used to extract time series crowd counts from images captured over 16 days at 5-minute intervals. These extracted time series data are then used for training multiple Long Short-Term Memory (LSTM) variants to analyze the dynamics of crowd forecasting. Experimental results demonstrate that the proposed framework enables accurate crowd forecasting while reducing annotation costs. Unlike existing vision-based approaches, which are constrained to forecasting seconds ahead, our approach can forecast a horizon of one hour ahead. Notably, the CNN Autoencoder LSTM and ConvLSTM models achieved an RMSE of 61.93 and a MAPE of 26.13%. These findings highlight the effectiveness of semi-supervised learning with minimal labeled data in vision-based crowd forecasting. Future work will focus on improving generalizability and robustness across different urban environments.
IEEE AccessCOMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
9.80
自引率
7.70%
发文量
6673
审稿时长
6 weeks
期刊介绍:
IEEE Access® is a multidisciplinary, open access (OA), applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE''s fields of interest.
IEEE Access will publish articles that are of high interest to readers, original, technically correct, and clearly presented. Supported by author publication charges (APC), its hallmarks are a rapid peer review and publication process with open access to all readers. Unlike IEEE''s traditional Transactions or Journals, reviews are "binary", in that reviewers will either Accept or Reject an article in the form it is submitted in order to achieve rapid turnaround. Especially encouraged are submissions on:
Multidisciplinary topics, or applications-oriented articles and negative results that do not fit within the scope of IEEE''s traditional journals.
Practical articles discussing new experiments or measurement techniques, interesting solutions to engineering.
Development of new or improved fabrication or manufacturing techniques.
Reviews or survey articles of new or evolving fields oriented to assist others in understanding the new area.