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和服务中。视频分析的前沿是用户为查找特定感兴趣对象而开发的视频查询。基于视频对象（如人、动物、汽车等）——这一视频分析的核心——在精神上与面向对象传统语言建模的对象相似，我们提出了一种面向对象的视频分析方法。该方法名为VQPy，包含一个前端——一种Python变体，其构造特性使用户能够轻松表达视频对象及其交互——以及一个可扩展的后端，该后端能够基于视频对象自动构建和优化流水线。我们已实现并开源了VQPy，并已在思科作为其DeepVision框架的一部分投入产品化使用。