Deep learning-based crowd recognition for tourist attractions in different periods

Xiaoyan Fang

doi:10.1117/12.3001368

9 August 2023 Deep learning-based crowd recognition for tourist attractions in different periods

Xiaoyan Fang

Proceedings Volume 12782, Third International Conference on Image Processing and Intelligent Control (IPIC 2023); 1278219 (2023) https://doi.org/10.1117/12.3001368
Event: Third International Conference on Image Processing and Intelligent Control (IPIC 2023), 2023, Kuala Lumpur, Malaysia

Abstract

The accuracy of tourists traffic prediction plays a critical role in scenic area management. Traditional methods of forecasting tourist attraction traffic rely heavily on static historical data, often ignoring important factors that affect the flow of tourists. This process is usually time-consuming. However, with the emergence of deep technology, it is now possible to use real-time data collection and analysis to design a temporal and spatial representation of data sources. And a deep learning-based tourist flow recognition model combined with dynamic time-bending distance indicators and a temporal feature recognition method with temporal data clustering analysis is designed. The method can use location big data to analyze traffic temporal types and identify traffic spatial distribution features, and the analysis results can help traffic and facility management in scenic areas.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiaoyan Fang "Deep learning-based crowd recognition for tourist attractions in different periods", Proc. SPIE 12782, Third International Conference on Image Processing and Intelligent Control (IPIC 2023), 1278219 (9 August 2023); https://doi.org/10.1117/12.3001368

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