Next, we propose a new strategy for joint alignment that lets us ![]() Concretely, first we identify the difficultiesĪssociated with joint alignment of video frames in general and in a DL setting This paper proposes a new method, calledĭeepMCBM, that eliminates all the aforementioned issues and achieves Prevents the recognition and leveraging of cases where the camera revisits Unfortunately, theįormer creates several problems, including poor scalability, while the latter Typically-large panoramic image or in an online fashion. Moreover, existing MCBMs usually model the background either on the domain of a Unsupervised task, of end-to-end solutions based on deep learning (DL). These hurdles also impeded the employment, in this Thus, existing MCBMs are limited in their scope and their supportedĬamera-motion types. However, while learning such a model in a video captured byĪ static camera is a fairly-solved task, in the case of a Moving-cameraīackground Model (MCBM), the success has been far more modest due toĪlgorithmic and scalability challenges that arise due to the camera motion. ![]() Download a PDF of the paper titled A Deep Moving-camera Background Model, by Guy Erez and 2 other authors Download PDF Abstract: In video analysis, background models have many applications such asīackground/foreground separation, change detection, anomaly detection,
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