通过人脸识别时间戳分析视频中明星动态，揭示角色互动与出镜规律。
Visualizing Celebrity Dynamics in Video Content: A Proposed Approach Using Face Recognition Timestamp Data
- 用多卡并行+ONNX优化处理大规模视频，生成精确时间戳数据。
- 生成频次、时长、共现等10类可视化图表，展现明星影响力变化。
- 交互式平台支持探索关键剧情节点，适合内容创作者与研究者使用。
在视频内容主导的时代，理解其结构与动态愈发重要。本文提出一种混合框架，结合分布式多GPU推理系统与交互式可视化平台，分析视频剧集中明星的动态表现。推理框架通过优化的ONNX模型、异构批处理和高吞吐并行，高效处理大量视频数据，实现可扩展的时间戳出现记录生成。这些记录被转化为一系列可视化图表，包括出现频率图、时长分析图、饼图、共现矩阵、网络图、堆叠面积图、季节对比图及热力图，提供多维度洞察，揭示明星影响力、屏幕时间分布、时间动态、共现关系及跨季强度变化。系统具备交互性，支持用户动态探索数据，识别关键事件，发现人物间关系演变。该工作连接分布式识别与结构化视觉分析，为娱乐数据分析、内容创作策略与观众参与研究开辟新路径。
原文摘要 · Abstract (English)
In an era dominated by video content, understanding its structure and dynamics has become increasingly important. This paper presents a hybrid framework that combines a distributed multi-GPU inference system with an interactive visualization platform for analyzing celebrity dynamics in video episodes. The inference framework efficiently processes large volumes of video data by leveraging optimized ONNX models, heterogeneous batch inference, and high-throughput parallelism, ensuring scalable generation of timestamped appearance records. These records are then transformed into a comprehensive suite of visualizations, including appearance frequency charts, duration analyses, pie charts, co-appearance matrices, network graphs, stacked area charts, seasonal comparisons, and heatmaps. Together, these visualizations provide multi-dimensional insights into video content, revealing patterns in celebrity prominence, screen-time distribution, temporal dynamics, co-appearance relationships, and intensity across episodes and seasons. The interactive nature of the system allows users to dynamically explore data, identify key moments, and uncover evolving relationships between individuals. By bridging distributed recognition with structured, visually-driven analytics, this work enables new possibilities for entertainment analytics, content creation strategies, and audience engagement studies.
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