论文标题
通过演员批评算法的精美边缘检测的自适应阈值
An Adaptive Threshold for the Canny Edge Detection with Actor-Critic Algorithm
论文作者
论文摘要
视觉监视旨在执行强大的前景对象检测，而不论时间和地点如何。对象检测仅使用空间信息显示出良好的结果，但是视觉监视中的前景对象检测需要适当的时间和空间信息处理。在基于深度学习的前景对象检测算法中，在类似于训练的环境中，检测能力优于经典背景减法（BGS）算法。但是，性能低于与训练不同的环境中经典BGS算法的性能。本文提出了一个时空融合网络（STFN），该网络可以使用时间网络和空间网络提取时间和空间信息。我们建议使用半前景图进行稳定训练所提出的STFN的方法。所提出的算法在不同于培训的环境中表现出了出色的性能，我们通过使用各种公共数据集的实验来展示它。此外，STFN可以在半监督的方法中生成合规的背景图像，并且可以在带有GPU的桌面上实时运行。所提出的方法分别比Lasiesta和SBI数据集中的最新深度学习方法显示了11.28％和18.33％的FM。
Visual surveillance aims to perform robust foreground object detection regardless of the time and place. Object detection shows good results using only spatial information, but foreground object detection in visual surveillance requires proper temporal and spatial information processing. In deep learning-based foreground object detection algorithms, the detection ability is superior to classical background subtraction (BGS) algorithms in an environment similar to training. However, the performance is lower than that of the classical BGS algorithm in the environment different from training. This paper proposes a spatio-temporal fusion network (STFN) that could extract temporal and spatial information using a temporal network and a spatial network. We suggest a method using a semi-foreground map for stable training of the proposed STFN. The proposed algorithm shows excellent performance in an environment different from training, and we show it through experiments with various public datasets. Also, STFN can generate a compliant background image in a semi-supervised method, and it can operate in real-time on a desktop with GPU. The proposed method shows 11.28% and 18.33% higher FM than the latest deep learning method in the LASIESTA and SBI dataset, respectively.
