Deep Autoencoder Architectures For Foreground Object Detection In Video Sequences Based On Probabilistic Mixture Models

Foreground object detection algorithms should be insensitive to noise present in the analyzed video sequences. In this work, a study of a type of non-supervised deep learning network, called autoencoder, is performed. They are suited to reduce input dimensionality and capture the most relevant information present in a region or image. Therefore, different types of autoencoders, deterministic and variational, with different architectures, activation functions and number of layers, are analyzed. This neural network is combined with a probabilistic mixture model which attempts to classify each video frame region as background and foreground.