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.
翻译:图像变化检测是一个图像处理问题,涉及到将数字图像的像素分割成前景和背景区域。大多数基于视觉知识的计算机智能系统,如交通监测、视频监视和异常检测,都需要使用变化检测技术。在最突出的检测方法中,有学习型方法,除了共享类似的培训和测试协议外,在结构设计战略方面彼此不同。这种架构设计直接影响到检测结果的质量,以及设备资源能力,如记忆。在这项工作中,我们提议建立一个新型的多比例级级级级级级级级级级级残余神经网络,通过一个残余处理模块,将多尺度的处理战略整合在一起,并配有分层式的动态神经网络。在两个不同的数据集上进行的实验支持了拟议方法的有效性,在整体上实现了$\boldsybol{F\ text{}-度值}($\boldsymonbol{0.9622}和$\boldsymbol{0.96}和$\boldsymbol{0.96}。我们提议的一个新的多级级级级级级级的神经网络网络网络网络,通过一个分级处理模块模块处理模块,通过一个分级的处理模块处理模型网络网络。在大约八次的测测测测测测测测测测距中,而获得的模型测测测距中,分别是这四次测测测测测测距。