PEOPLE IDENTIFICATION AND TRACKING IN VIDEO FILES USING MACHINE AND DEEP LEARNING ALGORITHMS
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Keywords:
Viola-Jones method, Feature extraction, PCA, Deep learning, VGG16-CNN.Abstract
This paper presents a people identification and tracking system for video files designed to verify the presence of persons in the video file or not. The system consists of several stages: converting the video files into a set of frames, identifying face areas using the Viola-Jones method, and extracting characteristics of these faces based on two techniques; the first technique is deep feature extraction, which is the pre-trained network VGG16-CNN. The second technique is shallow feature extraction, represented by the Principal Component Analysis method, and classifying persons based on the multilayer perceptron neural network. The system is tested on five video files of different times, and each video contains different numbers of people. The results obtained through the confusion matrix showed that the system gives a high classification accuracy and a good detection rate when using the VGG16-CNN algorithm for feature extraction. Through measures of precision, recall, and accuracy, it is found that the system is strong and has a high detection rate. The system gave a high classification accuracy of 93% ,precision of 100%, and recall of 93%.
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