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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:
Neural Computing and Applications Aims and scope Submit manuscript

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.

References

  1. Boominathan L, Kruthiventi SS, Babu RV (2016) Crowdnet: a deep convolutional network for dense crowd counting. In: Proceedings of the 24th ACM international conference on Multimedia, pp 640–644

  2. Sajid U, Sajid H, Wang H, Wang G (2020) ZoomCount: a zooming mechanism for crowd counting in static images. IEEE Trans Circuits Syst Video Technol 30(10):3499–3512

    Article  Google Scholar 

  3. Sindagi VA, Patel VM (2018) A survey of recent advances in CNN-based single image crowd counting and density estimation. Pattern Recogn Lett 107:3–16

    Article  Google Scholar 

  4. Wang Q, Gao J, Lin W, Yuan Y (2019) Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 8198–8207

  5. Delussu R, Putzu L, Fumera G (2022) Scene-specific crowd counting using synthetic training images. Pattern Recogn 124:108484

    Article  Google Scholar 

  6. Wang Q, Gao J, Lin W, Li X (2020) NWPU-crowd: a large-scale benchmark for crowd counting and localization. IEEE Trans Pattern Anal Mach Intell 43(6):2141–2149

    Article  Google Scholar 

  7. Wang Y, Ma Z, Wei X, Zheng S, Wang Y, Hong X (2022) ECCNAS: efficient crowd counting neural architecture search. ACM Trans Multimed Comput Commun Appl 18(1):1s–19

    Article  Google Scholar 

  8. Liu YB, Jia RS, Liu QM, Zhang XL, Sun HM (2021) Crowd counting method based on the self-attention residual network. Appl Intell 51(1):427–440

    Article  Google Scholar 

  9. Chen X, Bin Y, Gao C, Sang N, Tang H (2020) Relevant region prediction for crowd counting. Neurocomputing 407:399–408

    Article  Google Scholar 

  10. Ilyas N, Shahzad A, Kim K (2019) Convolutional-neural network-based image crowd counting: review, categorization, analysis, and performance evaluation. Sensors 20(1):43

    Article  Google Scholar 

  11. Gao J, Yuan Y, Wang Q (2020) Feature-aware adaptation and density alignment for crowd counting in video surveillance. IEEE Trans Cybern 51(10):4822–4833

    Article  Google Scholar 

  12. Zhu L, Li C, Yang Z, Yuan K, Wang S (2020) Crowd density estimation based on classification activation map and patch density level. Neural Comput Appl 32:5105–5116

    Article  Google Scholar 

  13. Khan SD, Ullah H, Uzair M, Ullah M, Ullah R, Cheikh FA (2019) Disam: density independent and scale aware model for crowd counting and localization. In: 2019 IEEE international conference on image processing (ICIP). IEEE, pp 4474–4478

  14. Liu L, Qiu Z, Li G, Liu S, Ouyang W, Lin L (2019) Crowd counting with deep structured scale integration network. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 1774–1783

  15. Liu L, Chen J, Wu H, Li G, Li C, Lin L (2021) Cross-modal collaborative representation learning and a large-scale RGBT benchmark for crowd counting. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 4823–4833

  16. Bouhlel F, Mliki H, Hammami M (2021) Abnormal crowd density estimation in aerial images based on the deep and handcrafted features fusion. Expert Syst Appl 173:114656

    Article  Google Scholar 

  17. Miao Y, Han J, Gao Y, Zhang B (2019) ST-CNN: spatial-temporal convolutional neural network for crowd counting in videos. Pattern Recogn Lett 125:113–118

    Article  Google Scholar 

  18. Alrowais F, Alotaibi SS, Al-Wesabi FN, Negm N, Alabdan R, Marzouk R, Mehanna AS, Al DM (2022) Deep transfer learning enabled intelligent object detection for crowd density analysis on video surveillance systems. Appl Sci 12(13):6665. https://doi.org/10.3390/app12136665

    Article  Google Scholar 

  19. Martínez F, Martínez F, Jacinto E (2020) Performance evaluation of the NASNet convolutional network in the automatic identification of COVID-19. Int J Adv Sci Eng Inf Technol 10(2):662

    Article  Google Scholar 

  20. Bharati S, Podder P, Mondal M, Gandhi N (2020) Optimized NASNet for diagnosis of COVID-19 from lung CT images. In: International conference on intelligent systems design and applications. Springer, Cham, pp 647-656

  21. Li S, Chen H, Wang M, Heidari AA, Mirjalili S (2020) ‘Slime mould algorithm: a new method for stochastic optimization.’ Future Gener Comput Syst 111:300–323

    Article  Google Scholar 

  22. Li Y, Zhang X, Chen D (2018) CsrNet: dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1091–1100.

  23. Meng X, Liu Y, Gao X, Zhang H (2014) A new bio-inspired algorithm: chicken swarm optimization. In: International conference in swarm intelligence. Springer, Cham, pp 86-94

  24. Wang Q, Gao J, Lin W, Li X (2020) NWPU-crowd: a large-scale benchmark for crowd counting. arXiv:2001.03360

  25. Idrees H, Tayyab M, Athrey K, Zhang D, Al-Maadeed S, Rajpoot N (2018) Composition loss for counting, density map estimation and localization in dense crowds. arXiv:1808.01050

  26. Zhang C, Li H, Wang X, Yang X (2015) Cross-scene crowd counting via deep convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Boston

  27. Sharath MN, Rajesh TM, Patil M (2022) Design of optimal metaheuristics based pixel selection with homomorphic encryption technique for video steganography. Int J Inf Technol 14:2265–2274. https://doi.org/10.1007/s41870-022-01005-9

    Article  Google Scholar 

  28. Kavitha M, Sankara Babu B, Sumathy B, Jackulin T, Ramkumar N et al (2022) Convolutional neural networks-based video reconstruction and computation in digital twins. Intell Autom Soft Comput 34(3):1571–1586

    Article  Google Scholar 

  29. Khan K, Khan RU, Albattah W, Nayab D, Qamar AM, Habib S, Islam M (2021) Crowd counting using end-to-end semantic image segmentation. Electronics 10(11):1293

    Article  Google Scholar 

Download references

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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  2. Deepak Kumar Jain
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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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