  In this project we propose a CNN architecture to detect anomaly and
suspicious activities; the activities chosen for the project are running,
jumping and kicking in public places and carrying gun, bat and knife in public
places. With the trained model we compare it with the pre-existing models like
Yolo, vgg16, vgg19. The trained Model is then implemented for real time
detection and also used the. tflite format of the trained .h5 model to build an
android classification.
