Smart City Gnosys
Smart city article details
| Title | Detection Of Object Throwing Behavior In Surveillance Videos |
|---|---|
| ID_Doc | 19274 |
| Authors | Kersten I.P.C.; Akdag E.; Bondarev E.; De With P.H.N. |
| Year | 2023 |
| Published | IS and T International Symposium on Electronic Imaging Science and Technology, 35, 9 |
| DOI | http://dx.doi.org/10.2352/EI.2023.35.9.IPAS-286 |
| Abstract | Anomalous behavior detection is a challenging research area within computer vision. Progress in this area enables automated detection of dangerous behavior using surveillance camera feeds. A dangerous behavior that is often overlooked in other research is the throwing action in traffic flow, which is one of the unique requirements of our Smart City project to enhance public safety. This paper proposes a solution for throwing action detection in surveillance videos using deep learning. At present, datasets for throwing actions are not publicly available. To address the use-case of our Smart City project, we first generate the novel public 'Throwing Action' dataset, consisting of 271 videos of throwing actions performed by traffic participants, such as pedestrians, bicyclists, and car drivers, and 130 normal videos without throwing actions. Second, we compare the performance of different feature extractors for our anomaly detection method on the UCF-Crime and Throwing-Action datasets. The explored feature extractors are the Convolutional 3D (C3D) network, the Inflated 3D ConvNet (I3D) network, and the Multi-Fiber Network (MFNet). Finally, the performance of the anomaly detection algorithm is improved by applying the Adam optimizer instead of Adadelta, and proposing a mean normal loss function that covers the multitude of normal situations in traffic. Both aspects yield better anomaly detection performance. Besides this, the proposed mean normal loss function also lowers the false alarm rate on the combined dataset. The experimental results reach an area under the ROC curve of 86.10 for the Throwing-Action dataset, and 80.13 on the combined dataset, respectively. © 2023, Society for Imaging Science and Technology. |
| Author Keywords | Anomaly Detection; CNN; Throwing Actions; UCF-Crime |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
54214  | 0.871 | Waheed S.R.; Suaib N.M.; Rahim M.S.M.; Khan A.R.; Bahaj S.A.; Saba T. | Synergistic Integration Of Transfer Learning And Deep Learning For Enhanced Object Detection In Digital Images | IEEE Access, 12 (2024) |
| 937 | 0.87 | Nayak R.; Pati U.C.; Das S.K. | A Comprehensive Review On Deep Learning-Based Methods For Video Anomaly Detection | Image and Vision Computing, 106 (2021) |
| 44339 | 0.867 | Samaila Y.A.; Sebastian P.; Shuaibu A.N.; Azhar Ali S.S.; Muhammad S.A.; Yahya M.S.; Shuaibu I.; Sani I.M. | Real-Time Deep Anomaly Detection: An Overview Of Benchmark Datasets And Performance Metrics | Transportation Research Procedia, 84 (2025) |
| 61134 | 0.865 | Khan H.; Yuan X.; Qingge L.; Roy K. | Violence Detection From Industrial Surveillance Videos Using Deep Learning | IEEE Access, 13 (2025) |
| 61219 | 0.861 | Ullah W.; Hussain T.; Baik S.W. | Vision Transformer Attention With Multi-Reservoir Echo State Network For Anomaly Recognition | Information Processing and Management, 60, 3 (2023) |
| 12041 | 0.86 | Adewopo V.; Elsayed N.; Elsayed Z.; Ozer M.; Zekios C.L.; Abdelgawad A.; Bayoumi M. | Big Data And Deep Learning In Smart Cities: A Comprehensive Dataset For Ai-Driven Traffic Accident Detection And Computer Vision Systems | 2024 IEEE 3rd International Conference on Computing and Machine Intelligence, ICMI 2024 - Proceedings (2024) |
| 12042 | 0.86 | Adewopo V.; Elsayed N.; Elsayed Z.; Ozer M.; Zekios C.L.; Abdelgawad A.; Bayoumi M. | Big Data And Deep Learning In Smart Cities: A Comprehensive Dataset For Ai-Driven Traffic Accident Detection And Computer Vision Systems | Conference Proceedings - IEEE SOUTHEASTCON (2024) |
| 9107 | 0.859 | Raja R.; Sharma P.C.; Mahmood M.R.; Saini D.K. | Analysis Of Anomaly Detection In Surveillance Video: Recent Trends And Future Vision | Multimedia Tools and Applications, 82, 8 (2023) |
| 8959 | 0.859 | 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) |
| 3569 | 0.859 | Zhao Y.; Man K.L.; Smith J.; Guan S.-U. | A Novel Two-Stream Structure For Video Anomaly Detection In Smart City Management | Journal of Supercomputing, 78, 3 (2022) |