Digital video intrusion intelligent detection method based on narrowband Internet of Things and its application

Abstract

This paper proposes a digital video intrusion detection method based on Narrow Band Internet of Things (NB-IoT), and establishes a digital video intrusion detection system based on NB-IoT network and SVM intelligent classification algorithm. Firstly, the image is preprocessed by gradation processing and threshold transformation to extract the HOG feature extraction of human intrusion behavior in digital video frame images. Then, combined with the human intrusion HOG feature data, the SVM intelligent algorithm is used to classify the human intrusion behavior, so as to accurately classify the movements of walking, jumping, running and waving in video surveillance. Finally, the performance analysis of the algorithm finds that the classification time, classification accuracy and classification false positive rate of the model are tested. The classification time is 40.8 s, the shortest is 27 s, the classification accuracy is 87.65%, and the lowest is 83.7%. The false detection rate is up to 15%, both of which are less than 20%, indicating that the classification method has good accuracy and stability. Comparing the algorithm with other algorithms, the intrusion sensitivity, intrusion specificity and training speed of the model are 93.6%, 94.3%, and 19 s, respectively, which are better than other methods, which indicates that the model has good detection performance in the experimental stage.