WSEAS Transactions on Systems and Control
Print ISSN: 1991-8763, E-ISSN: 2224-2856
Volume 16, 2021
Detection of Abnormal Activity to Alert the Nearby Persons via M-DNN Based Surveillance System
Authors: ,
Abstract: In today's environment, video surveillance is critical. When artificial intelligence, machine learning, and deep learning were introduced into the system, the technology had progressed much too far. Different methods are in place using the above combinations to help distinguish various wary activities from the live tracking of footages. Human behavior is the most unpredictable, and determining whether it is suspicious or normal is quite tough. In a theoretical setting, a deep learning approach is utilized to detect suspicious or normal behavior and sends an alarm to the nearby people if suspicious activity is predicted. In this paper, data fusion technique is used for feature extraction which gives an accurate outcome. Moreover, the classes are classified by the well effective machine learning approach of modified deep neural network (M-DNN), that predicts the classes very well. The proposed method gains 95% accuracy, as well the advanced system is contrast with previous methods like artificial neural network (ANN), random forest (RF) and support vector machine (SVM). This approach is well fitted for dynamic and static conditions.
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Keywords: Data fusion, Feature extraction, Modified deep neural network (M-DNN), Video surveillance, Wary activities
Pages: 668-685
DOI: 10.37394/23203.2021.16.61