Smart City Gnosys
Smart city article details
| Title | Violent Video Recognition By Using Sequential Image Collage |
|---|---|
| ID_Doc | 61137 |
| Authors | Tu Y.-S.; Shen Y.-S.; Chan Y.Y.; Wang L.; Chen J. |
| Year | 2024 |
| Published | Sensors, 24, 6 |
| DOI | http://dx.doi.org/10.3390/s24061844 |
| Abstract | Identifying violent activities is important for ensuring the safety of society. Although the Transformer model contributes significantly to the field of behavior recognition, it often requires a substantial volume of data to perform well. Since existing datasets on violent behavior are currently lacking, it will be a challenge for Transformers to identify violent behavior with insufficient datasets. Additionally, Transformers are known to be computationally heavy and can sometimes overlook temporal features. To overcome these issues, an architecture named MLP-Mixer can be used to achieve comparable results with a smaller dataset. In this research, a special type of dataset to be fed into the MLP-Mixer called a sequential image collage (SIC) is proposed. This dataset is created by aggregating frames of video clips into image collages sequentially for the model to better understand the temporal features of violent behavior in videos. Three different public datasets, namely, the dataset of National Hockey League hockey fights, the dataset of smart-city CCTV violence detection, and the dataset of real-life violence situations were used to train the model. The results of the experiments proved that the model trained using the proposed SIC is capable of achieving high performance in violent behavior recognition with fewer parameters and FLOPs needed compared to other state-of-the-art models. © 2024 by the authors. |
| Author Keywords | behavioral sciences; computer architecture; image recognition; multilayer perceptrons; neurons; training; Transformers |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
6855  | 0.919 | Shen Y.-S.; Chen J. | Aggregated Spatio-Temporal Mlp-Mixer For Violence Recognition In Video Clips | Proceedings - 2023 6th International Symposium on Computer, Consumer and Control, IS3C 2023 (2023) |
| 19844 | 0.873 | Abdali A.R.; Aggar A.A. | Devtrv2: Enhanced Data-Efficient Video Transformer For Violence Detection | 2022 7th International Conference on Image, Vision and Computing, ICIVC 2022 (2022) |
| 22444 | 0.869 | Ren X.; Fan W.; Wang Y. | Efficiently Adapting Large Pre-Trained Models For Real-Time Violence Recognition In Smart City Surveillance | Journal of Real-Time Image Processing, 21, 4 (2024) |
| 57685 | 0.866 | Huszar V.D.; Adhikarla V.K.; Negyesi I.; Krasznay C. | Toward Fast And Accurate Violence Detection For Automated Video Surveillance Applications | IEEE Access, 11 (2023) |
| 1323 | 0.862 | Song Z.; Zhang W.; Chen D. | A Deep Fusion Network For Violence Recognition | Proceedings - 2022 4th International Conference on Intelligent Information Processing, IIP 2022 (2022) |
| 17204 | 0.858 | Abdali A.R. | Data Efficient Video Transformer For Violence Detection | 10th IEEE International Conference on Communication, Networks and Satellite, Comnetsat 2021 - Proceedings (2021) |
| 3466 | 0.857 | Elzein A.; Basaran E.; Yang Y.D.; Qaraqe M. | A Novel Multi-Scale Violence And Public Gathering Dataset For Crowd Behavior Classification | Frontiers in Computer Science, 6 (2024) |
| 8959 | 0.855 | Mumtaz N.; Ejaz N.; Habib S.; Mohsin S.M.; Tiwari P.; Band S.S.; Kumar N. | An Overview Of Violence Detection Techniques: Current Challenges And Future Directions | Artificial Intelligence Review, 56, 5 (2023) |
| 61134 | 0.854 | Khan H.; Yuan X.; Qingge L.; Roy K. | Violence Detection From Industrial Surveillance Videos Using Deep Learning | IEEE Access, 13 (2025) |
| 950 | 0.851 | Ullah F.U.M.; Obaidat M.S.; Ullah A.; Muhammad K.; Hijji M.; Baik S.W. | A Comprehensive Review On Vision-Based Violence Detection In Surveillance Videos | ACM Computing Surveys, 55, 10 (2023) |