Automated object detection systems are a key component of modern surveillance applications. These systems rely on computationally expensive computer vision algorithms that perform object detection on visual data recorded by surveillance cameras. Due to the security and safety implications of these systems, this visual data st be processed accurately and in real-time. However, many of the frames that are created by the surveillance cameras may be of low importance, providing little or no useful information to the object detection system. Sub-sampling surveillance data by prioritizing important camera frames can greatly reduce unnecessary computation. Consequently, several works have explored dynamic visual data sub-sampling using various modalities of information (ie. spatial or temporal information) for prioritization. Few works, however, have combined and evaluated different modalities of information together for real-time prioritization of visual surveillance data. This work evaluates several individual and combined prioritization metrics derived from different modalities of information for use with a modern deep learning-based object detection algorithm. Both processing time and object detection rate are measured and used to rank the prioritization metrics. A novel approach that uses the historical detection confidences created by the object detection algorithm was demonstrated to be the best standalone prioritization metric. Additionally, a novel ensemble method that uses a KNN regressor to combine the best of the previously evaluated metrics to create a dynamic prioritization method is presented. This ensemble approach is shown to increase the object detection rate by up to 60% as compared to a static sub-sampling baseline as demonstrated using three publicly available datasets. The increased object detection rate was achieved while meeting the real-time constraints of the automated object detection system.

自动化的物体检测系统是现代监视应用程序的关键组成部分。这些系统依赖计算上昂贵的计算机视觉算法，该算法对监视摄像机记录的视觉数据执行对象检测。由于这些系统的安全性，该视觉数据可以实时准确地处理。但是，由监视摄像机创建的许多帧的重要性可能不高，从而几乎没有或没有为物体检测系统提供有用的信息。通过优先处理重要的摄像机帧来对监视数据进行二次采样可以大大减少不必要的计算。因此，一些工作探索了使用各种信息形式（即空间或时间信息）进行优先级排序的动态视觉数据子采样。但是，很少有作品 已将信息的不同形式组合并评估在一起，以便对视觉监视数据进行实时优先排序。这项工作评估了从不同信息形式中得出的几个单独的和组合的优先级度量标准，可与基于现代深度学习的对象检测算法配合使用。测量处理时间和对象检测率，并将其用于对优先级度量标准进行排名。一种使用对象检测算法创建的历史检测置信度的新颖方法被证明是最好的独立优先级度量标准。此外，提出了一种新颖的集成方法，该方法使用KNN回归器结合先前评估的最佳指标来创建动态优先级排序方法。与使用三个可公开获得的数据集证明的静态子采样基线相比，该集成方法显示出将对象检测率提高了60％。在满足自动化对象检测系统的实时约束的同时，实现了更高的对象检测率。