Optical Flow-Based Feature Selection with Mosaicking and FrIFrO Inception V3 Algorithm for Video Violence Detection

Authors

  • Elakiya Vijayakumar Department of Computer Science and Engineering, Annamalai University, India
  • Aruna Puviarasan Department of Computer Science and Engineering, Annamalai University, India
  • Puviarasan Natarajan Department of Computer and Information Science, Annamalai University, India
  • Suresh Kumar Ramu Ganesan Department of Computer Science and Engineering, Rajiv Gandhi College of Engineering and Technology, India
Volume: 14 | Issue: 3 | Pages: 14475-14482 | June 2024 | https://doi.org/10.48084/etasr.7270

Abstract

Violence in recent years poses the biggest threat to society, which needs to be addressed by all means. Video-based Violence detection is very tough to discern when the person or things that are recipients of a violent act are in motion. Detection of violence in video content is a critical task with applications spanning security surveillance, content moderation, and public safety. Leveraging the power of deep learning, the Violence Guard Freeze-In Freeze-Out Inception V3(VGFrIFrOI3) deep learning model in conjunction with optical flow-based characteristics proposes an effective solution for automated violence detection in videos. This architecture is known for its efficiency and accuracy in image classification tasks and in extracting meaningful features from video frames. By fine-tuning Inception V3 on video datasets annotated for violent and non-violent actions, the network can be permitted to learn discriminative features that simplify the detection of any violent behavior. Furthermore, the aforementioned model incorporates temporal information by processing video frames sequentially and aggregating features across multiple frames using techniques, such as temporal convolutional networks or recurrent neural networks. To assess the performance of this approach, a performance comparison of the proposed model against already existing methods was conducted, demonstrating the model’s superior accuracy and robustness in detecting violent actions. The recommended approach not only offers a highly accurate solution for violence detection in video content but also provides insights into the potential of deep learning architectures like Inception V3 in addressing real-world challenges in video analysis and surveillance. The Mosaicking processing, additionally carried out in the pre-processing step, improves the algorithm performance by deploying space search minimization and optical flow-based feature extraction, aiming to extemporize accuracy.

Keywords:

deep learning, violence detection, optical flow, convolutional neural networks, InceptionV3, mosaicking

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How to Cite

[1]
E. Vijayakumar, A. Puviarasan, P. Natarajan, and S. K. R. Ganesan, “Optical Flow-Based Feature Selection with Mosaicking and FrIFrO Inception V3 Algorithm for Video Violence Detection ”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 3, pp. 14475–14482, Jun. 2024.

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