Investigating Methods of Searching for Key Frames in Video Flow with the Use of Neural Networks for Search Systems

Abstract

Various methods of video content data analysis are presented, compared, and evaluated in this paper. Due to the analysis, the most effective strategies for video data processing involve searching for key frames within the video stream. The examined methods are categorized into consistent comparison, global comparison based on clustering, and event/object-based methodologies. Key techniques such as sequence search, classification, frame decoding, and anomaly detection are singled out as particularly valuable for comparison and matching tasks. The research further reveals that artificial intelligence and machine learning-driven methods reign supreme in this domain, with deep learning approaches outperforming traditional techniques. The employment of convolutional neural networks and attention mechanisms to capture the temporal intricacies across variable scopes is especially noteworthy. Additionally, leveraging the Actor-Critic model within a Generative Adversarial Network framework has shown encouraging outcomes. A significant highlight of the study is the proposed approach which incorporates modified Independent Recurrent Neural Networks (IndRNN) complemented by an attention mechanism. The enhancement using mathematical tools, notably the standard deviation, for key frame detection, exemplifies the potential of integrating analytical instruments to refine the system's precision. Such advancements, as presented in this research, pave the way for substantial enhancements in information systems tailored for video content analysis and source identification.