Advancing Video Search Capabilities: Integrating Feedforward Neural Networks for Efficient Fragment-Based Retrieval
Nataliia Melnykova, Petro Pobereiko · Computer Design Systems Theory and Practice · 2024
In the context of rapidly increasing volumes of video data, the problem of their efficient search and analysis becomes more acute. This research aims to develop and test an innovative system to improve the speed and accuracy of video search, utilizing the capabilities of Deep Convolutional Neural Networks (DCNN) and Feedforward Neural Networks (FFNN). Within the methodology developed for this study, video data are processed through several sequential stages: from feature extraction to key frame identification and the formation of an abstract vector representation. Deep Convolutional Neural Networks are central to the system for image analysis and Feedforward Neural Networks for optimizing the search process. The main results of the study include an increase in video search efficiency by reducing data processing time and increasing the accuracy of identifying relevant fragments. The originality of the work lies in the integration of two types of neural networks for structured analysis of video data, which is a new step in the development of video search technologies. The practical significance of the research is expressed in the possibility of applying the developed system in various areas where fast and accurate video search is needed: from the media industry to security systems. The scope of further research includes adapting the system to specific types of video content and expanding the capabilities of artificial intelligence for a deeper understanding of video data.