Deep Learning and Graph-Based Indexing for Context-Aware Video Retrieval Systems
Dinh-Lam Pham, Lanh-Thanh Le, Jeong-Hyun Chang, Sang-Eun Ahn, Kyonghee Sun, Kyong-Sook Kim, Kwanghoon Pio Kim · 2025
Managing the CCTV recorded footage takes advantage of not only hardware resources but also labor costs in monitoring and searching content through vast amounts of video clips generated every minute from CCTV cameras. Many studies and practices were introduced to deal with this matter. However, several challenges still need to be solved when implementing a video retrieval system. This study explores the applications of deep learning and graphs to create indexed data in order to implement functions in a video retrieval system. In particular, in this paper, we proposed an approach using graph structure to index the content in CCTV videos after using deep learning algorithms to contextualize detected objects in frames from these videos. Furthermore, we extend the approach to manage all the CCTV videos using graph management approach. This strategy, on the one hand, takes advantage of graph structure, providing an intuitive view for the CCTV management centers in managing and searching content from the managed cameras. On the other hand, it offers easy and efficient retrieval of video content.