UAV surveillance for violence detection and individual identification

Violence detection and face recognition of the individuals involved in the violence has an influence that's noticeable on the development of automated video surveillance research. With increasing risks in society and insufficient staff to monitor them, there is an expanding demand for drones square measure and computerized video surveillance. Violence detection is expeditious and can be utilized as the method to selectively filter the surveillance videos, and identify or take note of the individual who is creating the anomaly. Individual identification from drone surveillance videos in a crowded area is difficult because of the expeditious movement, overlapping features, and bestrew backgrounds. The goal is to come with a better drone surveillance system that recognizes the violent individuals that are implicated in violence and evoke a distress signal so that fast help can be offered. This paper uses the currently developed techniques based on deep learning and proposed the concept of transfer learning using deep learning-based different hybrid models with LSTM for violence detection. Identifying individuals incriminated in violence from drone-captured images involves major issues in variations of human facial appearance, hence the paper uses a CNN model combined with image processing techniques. For testing, the drone captured video dataset is developed for an unconstrained environment. Ultimately, the features extracted from a hybrid of inception modules and residual blocks, with LSTM architecture yielded an accuracy of 97.33% and thereby proved to be noteworthy and thereby, demonstrating its superiority over other models that have been tested. For the individual identification module, the best accuracy of 99.20% obtained on our dataset, is a CNN model with residual blocks trained for face identification.