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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/2408.02033 |
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| _version_ | 1866910554981924864 |
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| author | Janani, Pooya Suratgar, Amirabolfazl Taghvaeipour, Afshin |
| author_facet | Janani, Pooya Suratgar, Amirabolfazl Taghvaeipour, Afshin |
| contents | This paper proposes a hybrid fusion-based deep learning approach based on two different modalities, audio and video, to improve human activity recognition and violence detection in public places. To take advantage of audiovisual fusion, late fusion, intermediate fusion, and hybrid fusion-based deep learning (HFBDL) are used and compared. Since the objective is to detect and recognize human violence in public places, Real-life violence situation (RLVS) dataset is expanded and used. Simulating results of HFBDL show 96.67\% accuracy on validation data, which is more accurate than the other state-of-the-art methods on this dataset. To showcase our model's ability in real-world scenarios, another dataset of 54 sounded videos of both violent and non-violent situations was recorded. The model could successfully detect 52 out of 54 videos correctly. The proposed method shows a promising performance on real scenarios. Thus, it can be used for human action recognition and violence detection in public places for security purposes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_02033 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Human Action Recognition and Violence Detection Through Deep Learning Audiovisual Fusion Janani, Pooya Suratgar, Amirabolfazl Taghvaeipour, Afshin Computer Vision and Pattern Recognition Machine Learning Multimedia Image and Video Processing 68T07 (Primary), 68T45, 62H30 (Secondary) I.2.10; I.4.9; H.5.1 This paper proposes a hybrid fusion-based deep learning approach based on two different modalities, audio and video, to improve human activity recognition and violence detection in public places. To take advantage of audiovisual fusion, late fusion, intermediate fusion, and hybrid fusion-based deep learning (HFBDL) are used and compared. Since the objective is to detect and recognize human violence in public places, Real-life violence situation (RLVS) dataset is expanded and used. Simulating results of HFBDL show 96.67\% accuracy on validation data, which is more accurate than the other state-of-the-art methods on this dataset. To showcase our model's ability in real-world scenarios, another dataset of 54 sounded videos of both violent and non-violent situations was recorded. The model could successfully detect 52 out of 54 videos correctly. The proposed method shows a promising performance on real scenarios. Thus, it can be used for human action recognition and violence detection in public places for security purposes. |
| title | Enhancing Human Action Recognition and Violence Detection Through Deep Learning Audiovisual Fusion |
| topic | Computer Vision and Pattern Recognition Machine Learning Multimedia Image and Video Processing 68T07 (Primary), 68T45, 62H30 (Secondary) I.2.10; I.4.9; H.5.1 |
| url | https://arxiv.org/abs/2408.02033 |