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Suspicious Activities Recognition in Video Sequences Using DarkNet-NasNet Optimal Deep Features

Safdar Khan1, Muhammad Attique Khan2, Jamal Hussain Shah1,*, Faheem Shehzad2, Taerang Kim3, Jae-Hyuk Cha3

1 Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt, 47040, Pakistan
2 Department of Computer Science, HITEC University, Taxila, 47080, Pakistan
3 Department of Computer Science, Hanyang University, Seoul, 04763, Korea

* Corresponding Author: Jamal Hussain Shah. Email: email

Computer Systems Science and Engineering 2023, 47(2), 2337-2360. https://doi.org/10.32604/csse.2023.040410

Abstract

Human Suspicious Activity Recognition (HSAR) is a critical and active research area in computer vision that relies on artificial intelligence reasoning. Significant advances have been made in this field recently due to important applications such as video surveillance. In video surveillance, humans are monitored through video cameras when doing suspicious activities such as kidnapping, fighting, snatching, and a few more. Although numerous techniques have been introduced in the literature for routine human actions (HAR), very few studies are available for HSAR. This study proposes a deep convolutional neural network (CNN) and optimal featuresbased framework for HSAR in video frames. The framework consists of various stages, including preprocessing video frames, fine-tuning deep models (Darknet 19 and Nasnet mobile) using transfer learning, serial-based feature fusion, feature selection via equilibrium feature optimizer, and neural network classifiers for classification. Fine-tuning two models using some hit and trial methods is the first challenge of this work that was later employed for feature extraction. Next, features are fused in a serial approach, and then an improved optimization method is proposed to select the best features. The proposed technique was evaluated on two action datasets, Hybrid-KTH01 and HybridKTH02, and achieved an accuracy of 99.8% and 99.7%, respectively. The proposed method exhibited higher precision compared to existing state-ofthe-art approaches.

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APA Style
Khan, S., Khan, M.A., Shah, J.H., Shehzad, F., Kim, T. et al. (2023). Suspicious activities recognition in video sequences using darknet-nasnet optimal deep features. Computer Systems Science and Engineering, 47(2), 2337-2360. https://doi.org/10.32604/csse.2023.040410
Vancouver Style
Khan S, Khan MA, Shah JH, Shehzad F, Kim T, Cha J. Suspicious activities recognition in video sequences using darknet-nasnet optimal deep features. Comput Syst Sci Eng. 2023;47(2):2337-2360 https://doi.org/10.32604/csse.2023.040410
IEEE Style
S. Khan, M.A. Khan, J.H. Shah, F. Shehzad, T. Kim, and J. Cha "Suspicious Activities Recognition in Video Sequences Using DarkNet-NasNet Optimal Deep Features," Comput. Syst. Sci. Eng., vol. 47, no. 2, pp. 2337-2360. 2023. https://doi.org/10.32604/csse.2023.040410



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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