Abstract-Background subtraction is usually based on lowlevel or hand-crafted features such as raw color components, gradients, or local binary patterns. As an improvement, we present a background subtraction algorithm based on spatial features learned with convolutional neural networks (ConvNets). Our algorithm uses a background model reduced to a single background image and a scene-specific training dataset to feed ConvNets that prove able to learn how to subtract the background from an input image patch. Experiments led on 2014 ChangeDetection.net dataset show that our ConvNet based algorithm at least reproduces the performance of state-of-the-art methods, and that it even outperforms them significantly when scene-specific knowledge is considered.
Braham and Droogenbroeck REF proposed a convolutional neural network for background subtraction.
Deep background subtraction with scene-specific convolutional neural networks
2016 International Conference on Systems, Signals and Image Processing (IWSSIP)
2016 International Conference on Systems, Signals and Image Processing (IWSSIP)
Computer Science