Intelligent Surveillance for Suspicious Human Activity Recognition Using Deep Learning

Abstract

The rapid growth of surveillance systems in public and private spaces has increased the demand for intelligent methods to automatically detect suspicious human activities. Traditional monitoring systems rely heavily on human operators, making them inefficient, time-consuming, and prone to errors. This project proposes a deep learning-based approach for recognizing suspicious human activities from surveillance video streams in real time. The system utilizes advanced computer vision techniques to analyze video frames and extract meaningful spatial and temporal features that represent human behavior. The proposed system employs deep learning models such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to classify activities and identify abnormal or suspicious actions such as violence, loitering, or unauthorized access. Once such activities are detected, the system generates alerts and notifies relevant authorities for immediate response. This automated approach enhances surveillance efficiency, reduces human workload, and improves overall public safety by enabling proactive threat detection.