Mobile Neural Architecture Search Network and Convolutional Long Short-Term Memory-Based Deep Features Toward Detecting Violence from Video

Recently, surveillance cameras are deployed in many public places to monitor human activities. Detecting violence in videos through automatic analysis means significant for law enforcement. But almost many monitoring systems require to manually identify violent scenes in the video which leads to slow response. However, violence detection is a challenging problem because of the broad definition of violence. In this work, we will concern with physical violence that involved two persons or more. This work proposed a novel method to detect violence using automated mobile neural architecture search network and convolution long short-term-memory to extract spatiotemporal features in the video, and then adding two types of pooling layers max and average pooling to capture richer features, standard scaling these features and reducing the dimension using linear discriminative analysis to remove redundant features, and making classifier algorithms working well in low dimension. For classification, we trained and tested various machine learning models which are random forest, support vector machine (SVM), and K-nearest neighbor classifiers. We develop a combined dataset that contains violence and non-violence scenes from public datasets: hockey, movie, and violent flow. The performance of the proposed method is evaluated on a combined dataset in addition to three benchmark datasets, hockey, movie, and violent flow datasets in terms of detection accuracy. The results of our model showed high performance in combined, movie, and violent flow datasets using SVM classifier with accuracies of 97.5%, 100%, and 96%, respectively, whereas in the hockey dataset, we achieve the best result of 99.3% using the random forest classifier.