Object tracking is one of the most important and fundamental disciplines of Computer Vision. Many Computer Vision applications require specific object tracking capabilities, including autonomous and smart vehicles, video surveillance, medical treatments, and many others. The OpenCV as one of the most popular libraries for Computer Vision includes several hundred Computer Vision algorithms. Object tracking tasks in the library can be roughly clustered in single and multiple object trackers. The library is widely used for real-time applications, but there are a lot of unanswered questions such as when to use a specific tracker, how to evaluate its performance, and for what kind of objects will the tracker yield the best results? In this paper, we evaluate 7 trackers implemented in OpenCV against the MOT20 dataset. The results are shown based on Multiple Object Tracking Accuracy (MOTA) and Multiple Object Tracking Precision (MOTP) metrics.
翻译:目标跟踪是计算机视野的最重要和最基本的学科之一。 许多计算机视野应用都需要特定的物体跟踪能力, 包括自主和智能车辆、 视频监视、 医疗和其他许多功能。 OpenCV是计算机视野最受欢迎的图书馆之一, 包括几百个计算机视野算法。 图书馆中的物体跟踪任务可以大致地集中在单个和多个对象跟踪器中。 该图书馆被广泛用于实时应用程序, 但有许多未回答的问题, 比如何时使用特定的跟踪器, 如何评价其性能, 以及跟踪器将产生什么类型的物体最佳结果? 在本文件中, 我们根据多物体跟踪精度(MOTA) 和多物体跟踪精确度(MOTP) 衡量器来评估在 Op CV 中执行的7个跟踪器 。 结果表明, 其结果以多物体跟踪精度(MOTA) 和多物体跟踪精度(MOTP) 衡量器为基础 。