RETRACTED ARTICLE: Toward trustworthy human suspicious activity detection from surveillance videos using deep learning

Abstract

In today’s world, suspicious or unusual activities express threats and danger to others. For the prevention of various security issues, an automatic video detection system is very important. It is difficult to consecutively monitor camera videos recorded in public places to detect any abnormal event, so an automated video detection system is needed. The study objective is to create an intelligent and trustworthy system that will take a video stream as input and detect what kind of suspicious activity is happening in that video to reduce the time that consumes watching the video. In this work, we use three models Convolutional Neural Network (CNN), GRU, and ConvLSTM model. These models are trained on the same dataset of 6 suspicious activities of humans that are: Running, Punching, Falling, Snatching, Kicking, and Shooting. The dataset consists of various videos related to each activity. Different deep learning techniques are applied in the proposed work: preprocessing, data annotation model training, and classification. The frames are extracted from the source video, and then features are calculated through the model in Inception v3, a Convolutional Neural Network variant. On the same dataset, the CNN model attains 91.55% accuracy, the ConvLSTM model attains 88.73% accuracy, and the GRU model attains 84.01% accuracy. The performance of the proposed models is evaluated using a confusion matrix, F1-Score, precision, and recall. The proposed model proved better than other models in terms of performance and accuracy. The findings of this study prove helpful unusual events by examining a person's abnormal behavior.

Two stream deep neural network based framework to detect abnormal human activities

“…This process involved training and evaluating deep learning models with over 300 layers. In their work, Buttar et al 30 introduced a reliable and expert system designed to analyze video streams and detect suspicious activities. The system utilized three models, namely CNN, GRU, and ConvLSTM.…”

Smart Crowd Monitoring and Suspicious Behavior Detection Using Deep Learning

“…Because their net could only handle one-dimensional input strings, Matan et al used Viterbi decoding to obtain their outputs. By extending convnet outputs [31] to 2-dimensional maps of detection scores for postal address block corners, Wolf and Platt. These two earlier works use detection using complete convolutional learning and inference.…”