Despite recent advances in Computer Vision and Artificial Intelligence (AI), AI-assisted video solutions have struggled to penetrate real-world urban environments due to significant concerns regarding privacy, ethical risks, and technical challenges like bias and explainability. This work addresses these barriers through a case study in a city-center public market, demonstrating a pathway for the responsible deployment of AI in community spaces. By adopting a user-centric methodology that prioritizes public trust and privacy safeguards, we show that detailed, operationally relevant behavioral insights can be derived from abstract data representations without compromising ethical standards. The study focuses on generating Multi-Metric Behavioral Insights through the extraction of three complementary signals: customer directional flow, dwell duration, and movement patterns. Utilizing human pose detection and complex behavioral analysis - processed through geometric normalization and motion modeling - the system remains robust under tracking fragmentation and occlusion. Data collected over 18 days, spanning routine operations and a festival window from May 2-4, reveals a consistently right-skewed dwell-time behavior. While most visits last approximately 3-4 minutes, peak activity periods increase the mean to roughly 22 minutes. Furthermore, movement analysis indicates uneven circulation, with over 60% of traffic concentrated in approximately 30% of the venue space. By mapping popular thoroughfares and high-traffic storefronts, this case study provides venue managers and business owners with objective, measurable information to optimize foot traffic. Ultimately, these results demonstrate that AI-enabled video solutions can be successfully integrated into urban environments to provide high-fidelity spatial analytics while maintaining strict adherence to privacy and social responsibility.

翻译：尽管计算机视觉与人工智能（AI）领域近期取得了进展，但由于对隐私、伦理风险以及偏见与可解释性等技术挑战的严重担忧，AI辅助视频解决方案在现实城市环境中的渗透仍面临困难。本研究通过一个市中心公共市场的案例，为社区空间中负责任地部署AI提供了一条可行路径。通过采用以用户为中心、优先保障公众信任与隐私防护的方法论，我们证明无需牺牲伦理标准即可从抽象数据表征中提取详细且具有运营相关性的行为洞察。本研究聚焦于通过提取三个互补信号来生成多指标行为洞察：顾客流向、驻留时长与移动模式。利用人体姿态检测与复杂行为分析——通过几何归一化与运动建模进行处理——该系统在跟踪碎片化与遮挡条件下仍保持鲁棒性。历时18天（涵盖日常运营及5月2-4日的节庆时段）收集的数据显示，驻留时间行为呈现持续右偏分布。虽然大多数访问时长约为3-4分钟，但高峰活动时段将平均驻留时间提升至约22分钟。此外，移动分析表明客流分布不均，超过60%的人流集中在约30%的场地空间内。通过绘制热门通道与高流量店铺区域，本案例研究为场馆管理者与商户提供了客观可量化的信息以优化客流动线。最终，这些结果表明AI赋能的视频解决方案能够成功融入城市环境，在严格遵循隐私保护与社会责任的前提下，提供高保真度的空间分析数据。