Outdoor surveillance videos summarization model utilizing CNN-GRU

Surveillance video processing requires high efficiency, given its large datasets, demands significant resources for timely and effective analysis. This study aims to enhance surveillance systems by developing an automated method for extracting key events from outdoor surveillance videos. The proposed model comprises four phases: preprocessing and feature extraction, training and testing, and validation. Before utilizing a convolution neural networks approach to extract features from videos, the videos are pre-processed. Events classification uses gated recurrent units. In validation, motions and objects are extraction then feature extraction. Results show satisfactory performance, achieving 79% accuracy in events classification, highlighting the effectiveness of the methodology in identifying significant outdoor events.