Data Efficient Video Transformer for Violence Detection

In smart cities, violence event detection is critical to ensure city safety. Several studies have been done on this topic with a focus on 2d-Convolutional Neural Network (2d-CNN) to detect spatial features from each frame, followed by one of the Recurrent Neural Networks (RNN) variants as a temporal features learning method. On the other hand, the transformer network has achieved a great result in many areas. The bottleneck for transformers is the need for large data set to achieve good results. In this work, we propose a data-efficient video transformer (DeVTr) based on the transformer network as a Spatio-temporal learning method with a pre-trained 2d-Convolutional neural network (2d-CNN) as an embedding layer for the input data. The model has been trained and tested on the Real-life violence dataset (RLVS) and achieved an accuracy of 96.25%. A comparison of the result for the suggested method with previous techniques illustrated that the suggested method provides the best result among all the other studies for violence event detection.