融合改进MobileFaceNet与时序关联的影视人物身份追踪算法OA

With the surge in unstructured video data within film and television production,efficiently and accurately extract-ing footage of specific characters from complex scenes has become a major challenge in post-production.Traditional identi-fication methods often fail when confronted with special effects makeup,drastic lighting changes,and frequent montage-style editing,resulting in fragmented character trajectories.This study proposes a character identity tracking algorithm that integrates an improved MobileFaceNet with temporal correlation logic.By embedding a convolutional block attention mechanism into a lightweight MobileFaceNet network,the model's feature discrimination capability is enhanced under com-plex makeup conditions.Simultaneously,the study improves a cascaded matching strategy that incorporates motion predic-tion and appearance feature similarity,and analyzes montage editing to design an identity re-identification method based on a global feature library,effectively addressing the problem of trajectory fragmentation.Experiments on a self-built film and television video dataset show that the proposed method achieves a multi-target tracking accuracy of 78.9%and an overall identity score of 78.8%.Notably,the model significantly reduces errors caused by frequent identity switching while maintaining a fast inference speed,thereby meeting the high-precision and real-time requirements of the film and television industry for material indexing.