Identification and monitoring of violent interactions in video

This project shall help to bring a tool to fight against bullying in schools. It is also possible to use it in different scenes where a camera is recording a common area shared by people, such as companies, banks, prisons, or hospitals. To achieve that, the issue is approached from two main modules. The first one, a comparative study of approaches to detect violence in video, using image and video analyser Neural Networks (NN)s: a custom image analyser NN based on LeNet5, AlexNet, custom stacked long short-term memory (LSTM) and convolutional LSTM based NNs. The trainings are done with two datasets that have been subject to modifications to correct possible misinterpretations during the learning and pretraining is applied. The LeNet5 based NN is unsuccessful and tested with an independent dataset AlexNet is inaccurate. The best results are obtained with a stacked LSTM NN and a convolutional LSTM with dropout and a LSTM layer. Both NNs achieve over 90 % of accuracy with training and validation datasets, meanwhile the stacked LSTM and the convolutional NN achieve, respectively, 75 % and 100 % of accuracy with a small independent test dataset created. The convolutional LSTM needed 10 times less epochs to achieve the same result as the stacked LSTM. The second module consists of a violence detection system that applies the best solution obtained from the comparative study. The violence detection system saves the frames detected as violence with date, time and camera name and emits a sound alarm when more than a certain number of consecutive frames are evaluated as containing violence. This way the sensitivity of the system is reduced and avoids false alarms due to small mistakes done by the intelligence