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Slime Mold optimization with hybrid deep learning enabled crowd-counting approach in video surveillance

  • S.I.: Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIoT 2022)
  • Published:
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Abstract

Crowd counting (CC) and density estimation are crucial for ensuring public safety and security in surveillance videos with large audiences. As computer vision-based scene interpretation advances, automatic analysis of crowd situations is becoming increasingly prevalent. However, existing crowd analysis algorithms may not accurately interpret the video footage. To address this challenge, we propose a new approach called SMOHDL-CCA. This approach combines a Slime Mold Optimization algorithm with a Hybrid Deep Learning Enabled CC Approach. Our system uses the SMO algorithm with an optimized neural network search network (NASNet) model as the front-end to take advantage of transfer learning and flexible characteristics. The back-end model employs Dilated Convolutional Neural Networks, and the hyperparameter tuning process is done using the Chicken Swarm Optimization algorithm. Given a crowded video input frame, our SMOHDL-CCA model estimates the density map of the image. Each pixel value indicates the crowd density at the corresponding location in the picture. The final crowd count is obtained by summing all the values in the density map. We evaluated our proposed approach using three standard datasets. Furthermore, the state-of-the-art combining the proposed SMOHDL-CCA model achieves comparable performance such as improved precision is 96.97%, recall is 96.94%, and F1 score is 96.61%, reduced mean squared error of 61.15 values for the NWPU-crowd, UCF_QNRF, and World Expo datasets.

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Data availability

The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.

Code availability

The codes generated during the current study are available from the corresponding author upon reasonable request.

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Funding

Supported by the Open Project Program of Shanghai Innovation Center of Reverse Logistics and Supply Chain.

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Authors and Affiliations

  1. School of Computer and Information Engineering, Institute for Artificial Intelligence, Shanghai Innovation Center of Reverse Logistics and Supply Chain, Shanghai Polytechnic University, Shanghai, 201209, China

    Zheng Xu

  2. Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian, 116024, China

    Deepak Kumar Jain

  3. School of Artificial Intelligence, Dalian, China

    Deepak Kumar Jain

  4. Symbiosis Institute of Technology, Symbiosis International University, Pune, India

    Deepak Kumar Jain

  5. School of Computer Science, East China Normal University, Shanghai, China

    Pourya Shamsolmoali

  6. Department of Industrial Engineering and Future Studies, Faculty of Engineering, University of Isfahan, Isfahan, 81746-73441, Iran

    Alireza Goli

  7. Department of Computer Science and Engineering, R.M.K Engineering College, Kavaraipettai, 601206, India

    Subramani Neelakandan

  8. Department of Civil Engineering, Symbiosis Institute of Technology, Symbiosis International University, Pune, Maharashtra, 412115, India

    Amar Jain

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  1. Zheng Xu
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Correspondence to Deepak Kumar Jain.

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Xu, Z., Jain, D.K., Shamsolmoali, P. et al. Slime Mold optimization with hybrid deep learning enabled crowd-counting approach in video surveillance. Neural Comput & Applic 36, 2215–2229 (2024). https://doi.org/10.1007/s00521-023-09083-x

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  • DOI: https://doi.org/10.1007/s00521-023-09083-x

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