Human Crime Based Intrusion Detection by Semantic Features Using LSTM with Inception Deep Learning Approach
Keywords:
Intrusion Detection, Crime-based Intrusion Detection, Semantic Features, Deep LearningAbstract
A technique that is based on Deep Learning (DL) is presented here in order to categorize human activities based on the video data. In the area of computer vision, the use of image and video categorization has recently shown considerable progress thanks to the use of convolutional neural networks (CNN). CNN conducts research and analysis on new developments in its network architecture. A method for the classification of human activities is proposed, and its foundations are the structural characteristics of CNNs that have been investigated, as well as the levels of accuracy attained by various architectural configurations during the Image Large Scale Visual Recognition Challenge (ILSVRC). In addition to the spatial correlation that is seen in 2D pictures, the correlation that is seen in the temporal domain is also owned by video data. Incept LSTM is the name of the suggested approach, and it is built on both Inception and LSTM. The approach that has been suggested is capable of accurately recognizing human actions. In addition, the significance of hyper-parameter adjustment has been investigated and applied. The data from the UCF-Crime dataset was used to train and verify the approach that is being suggested. The findings of the experiments provide evidence that the suggested technique is capable of accurately identifying human activities in movies.
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