Actions detection in video Using Deep Learning

Abstract

The security system gets better with Suspicious Activity Detection using CNNs because this method automatically detects deviations from normal behavior through video analysis. A Convolutional Neural Networks (CNNs) type of deep learning technology enables staffless video surveillance by detecting trespassing and loitering incidents along with aggressive behavior. The security network learns to identify between commonplace and alarming behaviors during its operational period. The system builds its capabilities through video training that combines regular daily activities with security threats. After the training process the model demonstrates the capability to identify security breaches with efficiency. The unique selling point of this solution is its quick processing time combined with high accuracy while operating in real-time. The system functions across diverse environments because the researchers designed it to operate whether light conditions change or cameras are situated differently or background sounds vary. System testing with genuine surveillance video demonstrated its operation success throughout multiple realistic scenarios.