Detection of Object Throwing Behavior in Surveillance Videos

• URL: http://arxiv.org/abs/2403.06552v1
• Date: Mon, 11 Mar 2024 09:53:19 GMT
• Title: Detection of Object Throwing Behavior in Surveillance Videos
• Authors: Ivo P.C. Kersten, Erkut Akdag, Egor Bondarev, Peter H. N. De With
• Abstract summary: This paper proposes a solution for throwing action detection in surveillance videos using deep learning. To address the use-case of our Smart City project, we first generate the novel public 'Throwing Action' dataset. We compare the performance of different feature extractors for our anomaly detection method on the UCF-Crime and Throwing-Action datasets.
• Score: 8.841708075914353
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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