Improving the functional attributes of urban communities to enhance their resilience in the face of complexity and uncertainty is crucial for urban managers and designers.
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.
Blogger's Review: CommuniWave offers an innovative approach to quantifying informal behaviors of urban residents through advanced machine learning techniques. This not only aids in scientific urban planning but also effectively enhances community resilience, making it worthy of wider application in urban settings. Its methodology and technical implementation provide valuable references for future research.