论文标题
场景更改检测使用多尺度级联剩余卷积神经网络
Scene Change Detection Using Multiscale Cascade Residual Convolutional Neural Networks
论文作者
论文摘要
场景变化检测是与将数字图像的像素分配到前景和背景区域有关的图像处理问题。通常，基于视觉知识的计算机智能系统，例如流量监视，视频监视和异常检测，需要使用更改检测技术。在最突出的检测方法中，有基于学习的检测方法，除了共享类似的培训和测试协议外，它们在其体系结构设计策略方面彼此不同。这种体系结构设计直接影响检测结果的质量以及设备资源能力，例如内存。在这项工作中，我们提出了一个新型的多尺度级联剩余卷积神经网络，该卷积神经网络通过残余处理模块与分割卷积神经网络整合了多尺度处理策略。在两个不同数据集上进行的实验支持拟议方法的有效性，从而达到$ \ boldsymbol {0.9622} $的$ \ boldsymbol {f \ text { - } superion { - } measure {0.9622} $ {0.9622} $和$ \ boldsymbol {0.9664} $ {0.9664} $ {0.9664} $ {0.9664} $ {0.9664} $ {0.9664} $ {0.9664} $ {0.9664} $均次数均更少。 参数。这种获得的结果将提出的技术置于前四个最新场景更改检测方法中。
Scene change detection is an image processing problem related to partitioning pixels of a digital image into foreground and background regions. Mostly, visual knowledge-based computer intelligent systems, like traffic monitoring, video surveillance, and anomaly detection, need to use change detection techniques. Amongst the most prominent detection methods, there are the learning-based ones, which besides sharing similar training and testing protocols, differ from each other in terms of their architecture design strategies. Such architecture design directly impacts on the quality of the detection results, and also in the device resources capacity, like memory. In this work, we propose a novel Multiscale Cascade Residual Convolutional Neural Network that integrates multiscale processing strategy through a Residual Processing Module, with a Segmentation Convolutional Neural Network. Experiments conducted on two different datasets support the effectiveness of the proposed approach, achieving average overall $\boldsymbol{F\text{-}measure}$ results of $\boldsymbol{0.9622}$ and $\boldsymbol{0.9664}$ over Change Detection 2014 and PetrobrasROUTES datasets respectively, besides comprising approximately eight times fewer parameters. Such obtained results place the proposed technique amongst the top four state-of-the-art scene change detection methods.
