Smart cities endeavor to deliver safe and sustainable infrastructure services that enable individuals, organizations, and communities alike to be productive, healthy, informed, and actively involved in rapid urbanization. The widespread installation of closed-circuit television cameras and continuously generated video streams are a strategic data source that can contribute toward safety and sustainability through efficient surveillance of smart city assets and resources. Recent advances in deep learning methods are able to detect and localize salient objects in a video stream. However, a number of practical issues remain unaddressed, such as suboptimality, latency, predictive accuracy, and most importantly the contextualization of all detected salient objects for informed decisions that aligns with ethical surveillance. In this article, we propose a Generative Latent Space (GenLS) approach that overcomes these challenges, specifically in road surveillance. We demonstrate an adaptation of this approach for a prominent use-case in road surveillance, License Plate Detection. GenLS was evaluated for accuracy, robustness, computational cost, and cogency, using a state-of-the-art benchmark dataset on road traffic. Results from these experiments and the corresponding ablation study validate GenLS and confirm its suitability for real-time smart city road surveillance.

中文翻译：

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智能城市实时道路监控的生成性潜在空间方法

智慧城市致力于提供安全和可持续的基础设施服务，使个人，组织和社区都能够高效，健康，明智地参与并积极参与快速的城市化进程。闭路电视摄像机的广泛安装和连续生成的视频流是一种战略性数据源，可以通过对智能城市资产和资源的有效监视来促进安全和可持续性。深度学习方法的最新进展能够检测和定位视频流中的显着对象。但是，许多实际问题仍未解决，例如次优性，潜伏期，预测准确性，最重要的是，所有检测到的显着对象的情境化与明智的决策相符，符合道德监督。在本文中，我们提出了一种可生成的潜在空间（GenLS）方法，可以克服这些挑战，特别是在道路监控方面。我们展示了这种方法在道路监控，车牌检测中的一个重要用例的改编。使用最新的道路交通基准数据集，对GenLS的准确性，鲁棒性，计算成本和协调性进行了评估。这些实验和相应的消融研究结果验证了GenLS，并证实了其适用于实时智能城市道路监控。使用最新的道路交通基准数据集。这些实验和相应的消融研究结果验证了GenLS，并证实了其适用于实时智能城市道路监控。使用最新的道路交通基准数据集。这些实验和相应的消融研究结果验证了GenLS，并证实了其适用于实时智能城市道路监控。