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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.02127 |
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| _version_ | 1866917854455005184 |
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| author | Pathak, Gourang Kumar, Abhay Rawat, Sannidhya Gupta, Shikha |
| author_facet | Pathak, Gourang Kumar, Abhay Rawat, Sannidhya Gupta, Shikha |
| contents | This paper addresses the challenge of automated violence detection in video frames captured by surveillance cameras, specifically focusing on classifying scenes as "fight" or "non-fight." This task is critical for enhancing unmanned security systems, online content filtering, and related applications. We propose an approach using a 3D Convolutional Neural Network (3D CNN)-based model named X3D to tackle this problem. Our approach incorporates pre-processing steps such as tube extraction, volume cropping, and frame aggregation, combined with clustering techniques, to accurately localize and classify fight scenes. Extensive experimentation demonstrates the effectiveness of our method in distinguishing violent from non-violent events, providing valuable insights for advancing practical violence detection systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_02127 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Streamlining Video Analysis for Efficient Violence Detection Pathak, Gourang Kumar, Abhay Rawat, Sannidhya Gupta, Shikha Computer Vision and Pattern Recognition This paper addresses the challenge of automated violence detection in video frames captured by surveillance cameras, specifically focusing on classifying scenes as "fight" or "non-fight." This task is critical for enhancing unmanned security systems, online content filtering, and related applications. We propose an approach using a 3D Convolutional Neural Network (3D CNN)-based model named X3D to tackle this problem. Our approach incorporates pre-processing steps such as tube extraction, volume cropping, and frame aggregation, combined with clustering techniques, to accurately localize and classify fight scenes. Extensive experimentation demonstrates the effectiveness of our method in distinguishing violent from non-violent events, providing valuable insights for advancing practical violence detection systems. |
| title | Streamlining Video Analysis for Efficient Violence Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.02127 |