BAHGRF3: Human gait recognition in the indoor environment using deep learning features fusion assisted framework and posterior probability moth flame optimisation
Abstract Biometric characteristics are playing a vital role in security for the last few years. Human gait classification in video sequences is an important biometrics attribute and is used for security purposes. A new framework for human gait classification in video sequences using deep learning (D...
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| Format: | Article |
| Language: | English |
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Wiley
2025-04-01
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| Series: | CAAI Transactions on Intelligence Technology |
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| Online Access: | https://doi.org/10.1049/cit2.12368 |
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| author | Muhammad Abrar Ahmad Khan Muhammad Attique Khan Ateeq Ur Rehman Ahmed Ibrahim Alzahrani Nasser Alalwan Deepak Gupta Saima Ahmed Rahin Yudong Zhang |
| author_facet | Muhammad Abrar Ahmad Khan Muhammad Attique Khan Ateeq Ur Rehman Ahmed Ibrahim Alzahrani Nasser Alalwan Deepak Gupta Saima Ahmed Rahin Yudong Zhang |
| author_sort | Muhammad Abrar Ahmad Khan |
| collection | DOAJ |
| description | Abstract Biometric characteristics are playing a vital role in security for the last few years. Human gait classification in video sequences is an important biometrics attribute and is used for security purposes. A new framework for human gait classification in video sequences using deep learning (DL) fusion assisted and posterior probability‐based moth flames optimization (MFO) is proposed. In the first step, the video frames are resized and fine‐tuned by two pre‐trained lightweight DL models, EfficientNetB0 and MobileNetV2. Both models are selected based on the top‐5 accuracy and less number of parameters. Later, both models are trained through deep transfer learning and extracted deep features fused using a voting scheme. In the last step, the authors develop a posterior probability‐based MFO feature selection algorithm to select the best features. The selected features are classified using several supervised learning methods. The CASIA‐B publicly available dataset has been employed for the experimental process. On this dataset, the authors selected six angles such as 0°, 18°, 90°, 108°, 162°, and 180° and obtained an average accuracy of 96.9%, 95.7%, 86.8%, 90.0%, 95.1%, and 99.7%. Results demonstrate comparable improvement in accuracy and significantly minimize the computational time with recent state‐of‐the‐art techniques. |
| format | Article |
| id | doaj-art-fd84fa8b8d874dbbad8f35d4c8b8ee66 |
| institution | OA Journals |
| issn | 2468-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | CAAI Transactions on Intelligence Technology |
| spelling | doaj-art-fd84fa8b8d874dbbad8f35d4c8b8ee662025-08-20T02:18:39ZengWileyCAAI Transactions on Intelligence Technology2468-23222025-04-0110238740110.1049/cit2.12368BAHGRF3: Human gait recognition in the indoor environment using deep learning features fusion assisted framework and posterior probability moth flame optimisationMuhammad Abrar Ahmad Khan0Muhammad Attique Khan1Ateeq Ur Rehman2Ahmed Ibrahim Alzahrani3Nasser Alalwan4Deepak Gupta5Saima Ahmed Rahin6Yudong Zhang7Foundation University Islamabad Islamabad PakistanDepartment of Artificial Intelligence, College of Computer Engineering and Science, Prince Mohammad Bin Fahd University, Al Khobar, Saudi ArabiaFoundation University Islamabad Islamabad PakistanComputer Science Department Community College King Saud University Riyadh Saudi ArabiaComputer Science Department Community College King Saud University Riyadh Saudi ArabiaDepartment of Computer Science Maharaja Agrasen Institute of Technology Delhi IndiaUnited International University Dhaka BangladeshSchool of Informatics University of Leicester Leicester UKAbstract Biometric characteristics are playing a vital role in security for the last few years. Human gait classification in video sequences is an important biometrics attribute and is used for security purposes. A new framework for human gait classification in video sequences using deep learning (DL) fusion assisted and posterior probability‐based moth flames optimization (MFO) is proposed. In the first step, the video frames are resized and fine‐tuned by two pre‐trained lightweight DL models, EfficientNetB0 and MobileNetV2. Both models are selected based on the top‐5 accuracy and less number of parameters. Later, both models are trained through deep transfer learning and extracted deep features fused using a voting scheme. In the last step, the authors develop a posterior probability‐based MFO feature selection algorithm to select the best features. The selected features are classified using several supervised learning methods. The CASIA‐B publicly available dataset has been employed for the experimental process. On this dataset, the authors selected six angles such as 0°, 18°, 90°, 108°, 162°, and 180° and obtained an average accuracy of 96.9%, 95.7%, 86.8%, 90.0%, 95.1%, and 99.7%. Results demonstrate comparable improvement in accuracy and significantly minimize the computational time with recent state‐of‐the‐art techniques.https://doi.org/10.1049/cit2.12368deep learningfeature fusionfeature optimizationgait classificationindoor environmentmachine learning |
| spellingShingle | Muhammad Abrar Ahmad Khan Muhammad Attique Khan Ateeq Ur Rehman Ahmed Ibrahim Alzahrani Nasser Alalwan Deepak Gupta Saima Ahmed Rahin Yudong Zhang BAHGRF3: Human gait recognition in the indoor environment using deep learning features fusion assisted framework and posterior probability moth flame optimisation CAAI Transactions on Intelligence Technology deep learning feature fusion feature optimization gait classification indoor environment machine learning |
| title | BAHGRF3: Human gait recognition in the indoor environment using deep learning features fusion assisted framework and posterior probability moth flame optimisation |
| title_full | BAHGRF3: Human gait recognition in the indoor environment using deep learning features fusion assisted framework and posterior probability moth flame optimisation |
| title_fullStr | BAHGRF3: Human gait recognition in the indoor environment using deep learning features fusion assisted framework and posterior probability moth flame optimisation |
| title_full_unstemmed | BAHGRF3: Human gait recognition in the indoor environment using deep learning features fusion assisted framework and posterior probability moth flame optimisation |
| title_short | BAHGRF3: Human gait recognition in the indoor environment using deep learning features fusion assisted framework and posterior probability moth flame optimisation |
| title_sort | bahgrf3 human gait recognition in the indoor environment using deep learning features fusion assisted framework and posterior probability moth flame optimisation |
| topic | deep learning feature fusion feature optimization gait classification indoor environment machine learning |
| url | https://doi.org/10.1049/cit2.12368 |
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