Abstract
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.
Abstract (translated)
在视频内容主导的时代，理解其结构和动态变得越来越重要。本文提出了一种结合分布式多GPU推理系统与交互式可视化平台的混合框架，用于分析视频剧集中名人的动态变化。 该推理框架通过利用优化后的ONNX模型、异构批量推理及高吞吐量并行计算来高效处理大量视频数据，确保可扩展生成带有时间戳的出现记录。然后将这些记录转换为一系列全面可视化的工具，包括出现频率图表、持续时间分析、饼图、共现矩阵、网络图、堆叠区域图、季节性比较和热力图。 通过这些可视化手段提供的多维度视角揭示了视频内容中名人的突出程度、屏幕时间分布、时间动态变化、共现关系以及在各集和季度内的强度。该系统的交互特性允许用户动态探索数据，识别关键时刻，并发现人物之间不断演变的关系。 通过将分布式识别人物与结构化且视觉驱动的分析相结合，这项工作为娱乐分析、内容创作策略及观众参与研究开启了新的可能性。
URL
https://arxiv.org/abs/2510.03292
