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FSC-Set: Counting, Localization of Football Supporters Crowd in the Stadiums

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submitted on 2023-08-31, 11:13 and posted on 2023-09-19, 10:14 authored by Omar Elharrouss, Noor Almaadeed, Khalid Abualsaud, Somaya Al-Maadeed, Ali Al-Ali, Amr Mohamed

Counting the number of people in a crowd has gained attention in the last decade. Due to its benefit to many applications such as crowd behavior analysis, crowd management, and video surveillance systems, etc. Counting crowded scenes, like stadiums, represents a challenging task due to the inherent occlusions and density of the crowd inside and outside the stadiums. Finding a pattern to control thousands of people and counting them is a challenging task. With the introduction of Convolutional Neural Networks (CNN), enables performing this task with acceptable performance. The accuracy of a CNN-based method is related to the size of data used for training. The availability of the dataset is sparse. In particular, there is no dataset in the literature that can be used for training applications for crowd scene. This paper proposes two main contributions including a new dataset for crowd counting, and a CNN-based method for counting the number of people and generating the crowd density maps. The proposed dataset for Football Supporters Crowd (FSC-Set) is composed of 6000 annotated images (manually) of different types of scenes that contain thousands of people gathering in or around the stadiums. FSC-Set contains more than 1.5 Million individuals. The collected images are captured under varying Fields of Views (FOV), illuminations, resolutions, and scales. The proposed dataset can also be utilized for other applications, such as individual’s localization and face detection as well as team recognition from supporter images. Further, we propose a CNN-based method named FSCNet for crowd counting exploiting context-aware attention, spatial-wise attention, and channel-wise attention modules. The proposed method is evaluated on our established FSC-Set and other existing datasets then compared to state-of-the-art methods. The obtained results show satisfactory performances on all the datasets. The dataset is made publicly available and can be requested using the following link:

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Published in: IEEE Access
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Open Access funding provided by the Qatar National Library.



  • English



Publication Year

  • 2022

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This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Qatar University
  • College of Engineering - QU
  • Supreme Committe for Delivery & Legacy