用视觉语言模型捕捉视频中动态人群，提升复杂群体检测准确率。
Dynamic Group Detection using VLM-augmented Temporal Groupness Graph
- 结合局部外观与全局场景信息，通过增强版CLIP提取特征。
- 构建全帧群组度图，通过全局优化实现时序一致性检测。
- 适用于人群行为分析、智能监控等需要动态群体识别的场景。
本文提出一种视频中动态人群检测方法。复杂群体检测不仅依赖个体局部外观特征，还需考虑场景全局上下文。本方法利用经群体检测增强的视觉语言模型（VLM），在每帧中提取局部与全局外观特征。为进一步提升性能，群体结构需在时间上保持一致。不同于以往假设群体不变的方法，本方法通过全局优化，构建包含所有帧群组度概率的图结构，实现对动态变化群体的检测。实验结果表明，该方法在公开数据集上优于现有最先进方法。代码已开源：https://github.com/irajisamurai/VLM-GroupDetection.git。
原文摘要 · Abstract (English)
This paper proposes dynamic human group detection in videos. For detecting complex groups, not only the local appearance features of in-group members but also the global context of the scene are important. Such local and global appearance features in each frame are extracted using a Vision-Language Model (VLM) augmented for group detection in our method. For further improvement, the group structure should be consistent over time. While previous methods are stabilized on the assumption that groups are not changed in a video, our method detects dynamically changing groups by global optimization using a graph with all frames' groupness probabilities estimated by our groupness-augmented CLIP features. Our experimental results demonstrate that our method outperforms state-of-the-art group detection methods on public datasets. Code: https://github.com/irajisamurai/VLM-GroupDetection.git
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