Scene Change Detection Using Multiscale Cascade Residual Convolutional Neural Networks
This addresses the problem of efficient and accurate scene change detection for applications like traffic monitoring and video surveillance, representing an incremental improvement with resource savings.
The paper tackles scene change detection by proposing a Multiscale Cascade Residual Convolutional Neural Network, achieving average F-measure results of 0.9622 and 0.9664 on two datasets with approximately eight times fewer parameters, placing it among the top four state-of-the-art methods.
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.