用视频分析居民非正式行为，帮城市管理者动态优化社区规划。
CommuniWave:A Machine Learning Model for Quantifying the Degree of Temporary Informal Behavior in Urban Communities
- 融合视频检测与随机森林，量化社区非正式行为程度。
- 通过街景视频生成行为波动图，实现动态监测。
- 适合城市规划、智能交通等需要实时反馈的场景。
针对城市管理者与设计者提升社区功能以应对复杂性与不确定性的需求，现有社区规划多采用自上而下的方式，缺乏有效指标量化居民的非正式行为，常导致与原计划冲突。本文提出CommuniWave，一种机器学习模型，用于高效检测并量化城市社区中的非正式行为程度（DIB）。该模型整合基于mmaction2的Behavior Capture Net（BCN）、自研YOLOv10模型（YLX）以及基于随机森林的Behavior Eval Model（BEM）。通过街景视频生成DIB波动图，实现对社区行为的动态监控，助力城市管理者进行精细化决策，提升社区整体韧性。
原文摘要 · Abstract (English)
For urban managers and designers, improving the functional attributes of urban communities to enhance territorial resilience in the face of complexity and uncertainty is crucial. Currently, community planning often follows a top-down approach and lacks effective metrics to quantify informal behaviors of residents, leading to frequent conflicts with original plans. This study introduces CommuniWave, a machine learning model designed to efficiently detect and quantify the Degree of Informal Behavior (DIB) in urban communities. The model integrates a Behavior Capture Net (BCN) based on mmaction2, a self-developed YOLOv10 model (YLX), and a Behavior Eval Model (BEM) using random forest. Ultimately, by generating DIB fluctuation charts from street videos, the model facilitates dynamic monitoring, supporting urban managers in making refined decisions to enhance the overall resilience of communities.
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