Video Face Tracking for IoT Big Data using Improved Swin Transformer based CSA Model

Anbumani K. Anitha C. Achuta Rao S.V. Praveen Kumar K. Ramasamy M. Mahaveerakannan R.
April 2024 AnaPub Publications

Journal of Machine and Computing
2024 #4 Issue 2 308 - 316 pp.

Even though Convolutional Neural Networks (CNNs) have greatly improved face-related algorithms, it is still difficult to keep both accuracy and efficiency in real-world applications. The most cutting-edge approaches use deeper networks to improve performance, but the increased computing complexity and number of parameters make them impractical for usage in mobile applications. To tackle these issues, this article presents a model for object detection that combines Deeplabv3+ with Swin transformer, which incorporates GLTB and Swin-Conv-Dspp (SCD). To start with, in order to lessen the impact of the hole phenomena and the loss of fine-grained data, we employ the SCD component, which is capable of efficiently extracting feature information from objects at various sizes. Secondly, in order to properly address the issue of challenging object recognition due to occlusion, the study builds a GLTB with a spatial pyramid pooling shuffle module. This module allows for the extraction of important detail information from the few noticeable pixels of the blocked objects. Crocodile search algorithm (CSA) enhances classification accuracy by properly selecting the models fine-tuning. On a benchmark dataset known as WFLW, the study experimentally validates the suggested model. Compared to other light models, the experimental findings show that it delivers higher performance with significantly fewer parameters and reduced computing complexity.

Convolutional Neural Networks , Crocodile Search Algorithm , Face Tracking , Global local Transformer Block , Pooling Shuffle Module , Spatial Pyramid

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Department of Electronics and Instrumentation Engineering, Sri Sairam Engineering College, Chennai, India
Department of Computer Science and Engineering, School of Computing, Mohan Babu University, Erstwhile Sree Vidyanikethan Engineering College, Andhra Pradesh, India
Data Science Research Laboratories, Sree Dattha Institute of Engineering & Science, Telangana, Sheriguda, India
Department of Information Technology, Kakatiya Institute of Technology and Science, Warangal, India
Department of Computing, De Montfort University Kazakhstan, Al-Farabi Ave, Kazakhstan
Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Tamil Nadu, Chennai, India

Department of Electronics and Instrumentation Engineering
Department of Computer Science and Engineering
Data Science Research Laboratories
Department of Information Technology
Department of Computing
Department of Computer Science and Engineering

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