论文标题
两栖动物：移动对象检测的自适应计算
AmphibianDetector: adaptive computation for moving objects detection
论文作者
论文摘要
卷积神经网络（CNN）允许在图像中实现对象检测任务的最高精度。进一步开发对象探测器的主要挑战是假阳性探测和加工能力的高需求。在本文中，我们提出了一种对象检测方法，该方法可以通过仅处理移动对象并降低算法推断所需的处理能力来减少假阳性检测的数量。提出的方法是对已经培训的对象检测任务的CNN修改。该方法可用于通过对算法应用小更改来提高现有系统的准确性。在开放数据集“ CDNET2014行人”中证明了该方法的效率。本文提出的方法的实施可在GitHub上获得：
Convolutional neural networks (CNN) allow achieving the highest accuracy for the task of object detection in images. Major challenges in further development of object detectors are false-positive detections and high demand of processing power. In this paper, we propose an approach to object detection which makes it possible to reduce the number of false-positive detections by processing only moving objects and reduce the required processing power for algorithm inference. The proposed approach is a modification of CNN already trained for object detection task. This method can be used to improve the accuracy of an existing system by applying minor changes to the algorithm. The efficiency of the proposed approach was demonstrated on the open dataset "CDNet2014 pedestrian". The implementation of the method proposed in the article is available on the GitHub: https://github.com/david-svitov/AmphibianDetector
