Enhancing Human Action Recognition and Violence Detection Through Deep Learning Audiovisual Fusion
This work addresses security concerns by improving violence detection in public places, though it is incremental as it builds on existing fusion methods.
The paper tackles human activity recognition and violence detection in public places by proposing a hybrid fusion-based deep learning approach using audio and video modalities, achieving 96.67% accuracy on a validation dataset and correctly detecting 52 out of 54 real-world videos.
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.