Video analytics is widely used in contemporary systems and services. At the forefront of video analytics are video queries that users develop to find objects of particular interest. Building upon the insight that video objects (e.g., human, animals, cars, etc.), the center of video analytics, are similar in spirit to objects modeled by traditional object-oriented languages, we propose to develop an object-oriented approach to video analytics. This approach, named VQPy, consists of a frontend$\unicode{x2015}$a Python variant with constructs that make it easy for users to express video objects and their interactions$\unicode{x2015}$as well as an extensible backend that can automatically construct and optimize pipelines based on video objects. We have implemented and open-sourced VQPy, which has been productized in Cisco as part of its DeepVision framework.

翻译：视频分析广泛应用于当代系统与服务中。其核心在于用户开发的视频查询，用以发现特定目标对象。基于视频对象（如人类、动物、车辆等）作为视频分析的核心要素，本质上与传统面向对象语言所建模的对象高度相似，我们提出了一种面向对象的视频分析方法。该方法名为VQPy，包含前端和后端两部分：前端是一个Python变体，通过结构化构造使用户能够便捷地描述视频对象及其交互；后端则是一个可扩展框架，能够基于视频对象自动构建和优化分析流水线。我们已实现并开源了VQPy，该工具已在思科作为其DeepVision框架的组成部分实现产品化。