Striking a Pose: DIY Computer Vision Sensor Kit to Measure Public Life Using Pose Estimation Enhanced Action Recognition Model

Observing and measuring public life is essential for designing inclusive, vibrant, and climate-resilient public spaces. While urban planners have traditionally relied on manual observation, recent advances in open-source Computer Vision (CV) now enable automated analysis. However, most CV sensors in urban studies focus on transportation analysis, offering limited insight into nuanced human behaviors such as sitting or socializing. This limitation stems in part from the challenges CV algorithms face in detecting subtle activities within public spaces. This study introduces the Public Life Sensor Kit (PLSK), an open-source, do-it-yourself system that integrates a GoPro camera with an NVIDIA Jetson edge device, and evaluates whether pose estimation-enhanced CV models can improve the detection of fine-grained public life behaviors, such as sitting and social interaction. The PLSK was deployed during a public space intervention project in Sydney, Australia. The resulting data were measured against data collected from the Vivacity sensor, a commercial transportation-focused CV system, and traditional human observation. The results show that the PLSK outperforms the commercial sensor in detecting and classifying key public life activities, including pedestrian traffic, sitting, and socializing. These findings highlight the potential of the PLSK to support ethically collected and behavior-rich public space analysis and advocate for its adoption in next-generation urban sensing technologies.