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Abnormal Behavior Detection in Video Surveillance Using Inception-v3 Transfer Learning Approaches | ||
| IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING | ||
| Article 16, Volume 23, Issue 2, June 2023, Pages 210-221 PDF (815.1 K) | ||
| Document Type: Research Paper | ||
| DOI: 10.33103/uot.ijccce.23.2.16 | ||
| Authors | ||
| Sabah Abdulazeez Jebur* 1; Khalid A. Hussein2; Haider Kadhim Hoomod3 | ||
| 1Department of Computer Sciences, University of Technology, Baghdad, Iraq | ||
| 2Department of Computer Science, College of Education, Mustansiriyah University, Baghdad , Iraq | ||
| 3Department of Computer Science, College of Education, Mustansiriyah University, Baghdad, Iraq | ||
| Abstract | ||
| The use of video surveillance systems has increased due to security concerns and their relatively low cost. Researchers are working to create intelligent Closed Circuit Television (CCTV) cameras that can automatically analyze behavior in real-time to detect anomalous behaviors and prevent dangerous accidents. Deep Learning (DL) approaches, particularly Convolutional Neural Networks (CNNs), have shown outstanding results in video analysis and anomaly detection. This research paper focused on using Inception-v3 transfer learning approaches to improve the accuracy and efficiency of abnormal behavior detection in video surveillance. The Inceptionv3 network is used to classify keyframes of a video as normal or abnormal behaviors by utilizing both pre-training and fine-tuning transfer learning approaches to extract features from the input data and develop a new classifier. The UCF-Crime dataset is used to train and evaluate the proposed models. The performance of both models was evaluated using accuracy, recall, precision, and F1 score. The fine-tuned model achieved 88.0%, 89.24%, 85.83%, and 87.50% for these measures, respectively. In contrast, the pre-trained model obtained 86.2%, 86.43%, 84.62%, and 85.52%, respectively. These results demonstrate that transfer learning using Inception-v3 architecture can effectively classify normal and abnormal behaviors in videos, and fine-tuning the weights of the layers can further improve the model's performance. | ||
| Keywords | ||
| Abnormal behavior detection; Video surveillance; Deep learning; Transfer learning; InceptionV3 | ||
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