INTELLIGENT VIDEO SURVELLIANCE SYSTEM

Intelligent video surveillance systems play a pivotal role in enhancing security across diverse environments, from public spaces to industrial facilities. This abstract introduces an innovative approach to video surveillance leveraging deep learning methodologies, specifically targeting anomaly detection within surveillance footage. The project centers around the development and deployment of a Spatial-temporal Autoencoder (STAE), a deep learning architecture specialized in capturing spatial and temporal patterns inherent in video data. The STAE model is engineered to encode spatial and temporal features extracted from input video frames, compressing them into a lower-dimensional latent space, and subsequently reconstructing the original frame. The STAE model is constructed utilizing leading deep learning frameworks such as Keras, incorporating elements such as 3D convolutional layers and Convolutional LSTM layers. Training of the model is executed on the preprocessed dataset, with parameter optimization facilitated through methodologies such as mean squared error loss minimization and early stopping. Upon completion of training, the STAE model is deployed within real-time surveillance systems, tasked with processing incoming video streams from surveillance cameras. Through this comprehensive approach, the project aims to enhance the effectiveness and efficiency of video surveillance systems in identifying and responding to anomalies, thereby bolstering security measures in diverse environments.