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Cluster-Based Monitoring and Location Estimation for Crowd Counting

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Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1301)

Abstract

Crowd management and monitoring are important research topics in order to ensure personal and community safety by making use of video images. The crowd tracking system (CMS) includes tasks such as density variation in images, irregular distribution of people and objects, overcrowding, exposure estimation. People come together for various purposes in areas designed for socializing, such as parks, stadiums, airports, hospitals, and shopping malls. Generally, these areas are monitored by closed-circuit TeleVision (CCTV). However, this type of system brings problems. The main ones are portability, flexible accessibility, limited coverage area, high power consumption. Crowd density is often examined by people for behavioral analysis or to identify suspicious people. Computer vision and deep learning approaches are maintained in order to prevent mistakes caused by human error and to make a faster evaluation.

In this study, a method for detecting event changes with cluster-based inspection in crowd images is proposed. Gaussian YOLOv3 model is used for object recognition. The proposed clustering approach is used to track changes in the number, coordinate, and direction information of the cluster. Behavior change time, location, and classification are achieved as a result of this information extractions. These two event changes are taken into consideration, especially because sudden changes in state occur in walking and running behavior. Six different video sequences in the PET2009 dataset are used for the study. Accuracy performance is achieved between 83.2% and 96.4%. The results obtained to achieve the success that can be compared with similar ones in the literature.

Keywords

  • Crowd counting
  • Crowd monitoring
  • Location estimation
  • Cluster-Based crowd analysis
  • Computer vision
  • Deep learning
  • Artificial intelligence

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  • DOI: 10.1007/978-3-030-66501-2_19
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Acknowledgments

The authors wish to acknowledge the support of the NVIDIA GPU Grant Program with the donation of the TITAN Xp GPU.

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Correspondence to Merve Ayyüce Kızrak .

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Kızrak, M.A., Bolat, B. (2021). Cluster-Based Monitoring and Location Estimation for Crowd Counting. In: Allahviranloo, T., Salahshour, S., Arica, N. (eds) Progress in Intelligent Decision Science. IDS 2020. Advances in Intelligent Systems and Computing, vol 1301. Springer, Cham. https://doi.org/10.1007/978-3-030-66501-2_19

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