Advances in deep neural networks (DNN) and computer vision (CV) algorithms have made it feasible to extract meaningful insights from large-scale deployments of urban cameras. Tracking an object of interest across the camera network in near real-time is a canonical problem. However, current tracking platforms have two key limitations: 1) They are monolithic, proprietary and lack the ability to rapidly incorporate sophisticated tracking models, and 2) They are less responsive to dynamism across wide-area computing resources that include edge, fog, and cloud abstractions. We address these gaps using Anveshak , a runtime platform for composing and coordinating distributed tracking applications. It provides a domain-specific dataflow programming model to intuitively compose a tracking application, supporting contemporary CV advances like query fusion and re-identification, and enabling dynamic scoping of the camera network's search space to avoid wasted computation. We also offer tunable batching and data-dropping strategies for dataflow blocks deployed on distributed resources to respond to network and compute variability. These balance the tracking accuracy, its real-time performance, and the active camera-set size. We illustrate the concise expressiveness of the programming model for four tracking applications. Our detailed experiments for a network of 1000 camera-feeds on modest resources exhibit the tunable scalability, performance, and quality trade-offs enabled by our dynamic tracking, batching, and dropping strategies.

中文翻译：

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跨多摄像机网络的分布式对象跟踪的可扩展平台

深度神经网络（DNN）和计算机视觉（CV）算法的进步使从城市摄像机的大规模部署中提取有意义的见解变得可行。几乎实时地跨摄像机网络跟踪感兴趣的对象是一个典型的问题。但是，当前的跟踪平台有两个主要局限性：1）它们是整体式的，专有的，并且缺乏快速整合复杂的跟踪模型的能力； 2）它们对包括边缘，模糊和模糊等广域计算资源的动态性反应较慢。云抽象。我们使用以下方法解决这些差距安韦沙克 ，这是一个用于组成和协调分布式跟踪应用程序的运行时平台。它提供了特定领域的数据流编程模型，以直观地构成跟踪应用程序，支持诸如查询融合和重新标识之类的当代CV改进，并实现了摄像机网络搜索空间的动态范围划分，从而避免了计算浪费。我们还为部署在分布式资源上的数据流块提供可调的批处理和数据丢弃策略，以响应网络和计算变化。这些平衡了跟踪精度，实时性能和有效的摄像机尺寸。我们说明了四个跟踪应用程序的编程模型的简洁表达。我们针对在有限资源上包含1000个摄像头馈送的网络进行的详细实验展示了可调的可扩展性，性能，