Anomaly Detection Based on Multisource CNN with Handcraft features

Today's latest video surveillance technology has recently been used to monitor human interactions in automated processing systems.They play an important part in security matters. Numerous difficulties problems in separating violent from nonviolent behaviour. Supernatural activities Like crowded areas and camera view. In this article, we recommend a deep novel technique detect violence against this structural system Built on particular traits resulting from artisan techniques and through which violence is detected. These features are related to the representation of the image, the appearance of the image, and their motion speed, and are fed as input to a neural network (CNN), which transforms them into spatial, temporal feature, and feature streams, trained a network through this spatial stream to recognize contextual patterns in each frame of video. This temporal stream consisted of three consecutive frames for learning each dynamic pattern of violent actions. Differential optical flow measurement. Furthermore, we added a distinguishing characteristic with a new kinetic a graphic of energy to depict violence acts distinct from others in a spatio-temporal stream. The approach incorporates different facets of violent actions by combining the outcomes of these streams is also called violence and trained label. They include hockey, movie and VP datasets that are crowded and not. These experimental results demonstrate that the proposed in terms of precision and speed of processing, the violence detection technique outperformed the prior study's results.