Name
Title | ||
|---|---|---|
Deep Autoencoder Architectures For Foreground Object Detection In Video Sequences Based On Probabilistic Mixture Models |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICIP40778.2020.9190834 | 2020 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) |
Keywords | DocType | ISSN |
foreground detection, deep autoencoders, video surveillance | Conference | 1522-4880 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jorge García-González | 1 | 0 | 0.34 |
| Miguel A. Molina-Cabello | 2 | 1 | 6.44 |
| Rafael Marcos Luque-Baena | 3 | 96 | 13.24 |
| Juan Miguel Ortiz-de-lazcano-lobato | 4 | 68 | 11.59 |
| Ezequiel López-Rubio | 5 | 323 | 39.73 |