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：视频分析广泛用于现代系统和服务中。用户通过开发视频查询来发现特定感兴趣的目标，这是视频分析的核心任务。基于视频对象（如人、动物、汽车等）在本质上与传统面向对象语言所建模的对象具有相似性的洞察，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——一种Python变体，其语言构造使用户能够便捷地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分析流水线。我们已实现并开源了VQPy，该工具已产品化并集成至思科DeepVision框架中。