Understanding how children use streets will allow urban planners to create streets that are livelier and more child-friendly. However, it is still challenging to investigate detailed information on children's movement behaviors on individual streets due to labor-intensive observations, the difficulty of integrating qualitative data, and privacy concerns. With the wide application of vision-based technologies, Researchers can analyze the movements and behaviors of large groups of children on streets automatically and continuously. This study aims to analyze children's movement behaviors on streets utilizing computer vision techniques in urban surveillance systems. The proposed methods automatically extract children's trajectories, calculate movement behavioral features, and classify the movement behavioral characteristics of children. Our results identify five movement behaviors of children on streets: walking, staying, running, accelerating, and decelerating, demonstrating their use of streets. In addition, the results showed the feasibility of computer vision techniques to identify differences in children's movement behaviors under varying conditions, such as day and time, and the potential of street environments to positively influence children's use of streets from the daily street lives of children. Utilizing vision-based analysis of children's movement behaviors provides meaningful information for improving streets for children toward a better understanding of the use of streets.

了解儿童如何使用街道将使城市规划者能够创造出更热闹、更适合儿童的街道。然而，由于劳动密集型观察、整合定性数据的困难以及隐私问题，调查个别街道上儿童运动行为的详细信息仍然具有挑战性。随着基于视觉的技术的广泛应用，研究人员可以自动、连续地分析街道上大批儿童的动作和行为。本研究旨在利用城市监控系统中的计算机视觉技术来分析儿童在街道上的运动行为。该方法自动提取儿童轨迹，计算运动行为特征，并对儿童运动行为特征进行分类。我们的结果识别了儿童在街道上的五种运动行为：步行、停留、跑步、加速和减速，展示了他们对街道的使用。此外，研究结果还表明，计算机视觉技术可以从儿童的日常街头生活中识别不同条件下（例如白天和时间）儿童运动行为的差异，以及街道环境对儿童使用街道产生积极影响的潜力。利用基于视觉的儿童运动行为分析可以为改善街道提供有意义的信息，让儿童更好地了解街道的使用。