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Abnormal Event Detection in a Surveillance Scene Using Convolutional Neural Network

Abnormal Event Detection in a Surveillance Scene Using Convolutional Neural Network

Kinjal V. Joshi, Narendra M. Patel
Copyright: © 2021 |Volume: 11 |Issue: 4 |Pages: 20
ISSN: 2155-6997|EISSN: 2155-6989|EISBN13: 9781799862055|DOI: 10.4018/IJCVIP.2021100101
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MLA

Joshi, Kinjal V., and Narendra M. Patel. "Abnormal Event Detection in a Surveillance Scene Using Convolutional Neural Network." IJCVIP vol.11, no.4 2021: pp.1-20. http://doi.org/10.4018/IJCVIP.2021100101

APA

Joshi, K. V. & Patel, N. M. (2021). Abnormal Event Detection in a Surveillance Scene Using Convolutional Neural Network. International Journal of Computer Vision and Image Processing (IJCVIP), 11(4), 1-20. http://doi.org/10.4018/IJCVIP.2021100101

Chicago

Joshi, Kinjal V., and Narendra M. Patel. "Abnormal Event Detection in a Surveillance Scene Using Convolutional Neural Network," International Journal of Computer Vision and Image Processing (IJCVIP) 11, no.4: 1-20. http://doi.org/10.4018/IJCVIP.2021100101

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Abstract

Automatic abnormal event detection in a surveillance scene is very significant because of more consciousness about public safety. Because of usefulness and complexity, currently, it is an open research area. In this manuscript, the authors have proposed a novel convolutional neural network (CNN) model to detect an abnormal event in a surveillance scene. In this work, CNN is used in two ways. Firstly, it is used for both feature extraction and classification. In a second way, CNN is used for feature extraction, and support vector machine (SVM) is used for classification. Without any pre-processing, the proposed model gives better results compared to state-of-the-art methods. Experiments are carried out on four different publicly available benchmark datasets and one combined dataset, which contains all images of four datasets. The performance is measured by accuracy and area under the ROC (receiver operating characteristic) curve (AUC). The experimental results determine the efficacy of the proposed model.

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