Moving Object Detection in Noisy Video Sequences Using Deep Convolutional Disentangled Representations

Noise robustness is crucial when approaching a moving detection problem since image noise is easily mistaken for movement. In order to deal with the noise, deep denoising autoencoders are commonly proposed to be applied on image patches with an inherent disadvantage with respect to the segmentation...

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Bibliographic Details
Main Authors: Garcia-Gonzalez, Jorge, Luque-Baena, Rafael M., Ortiz-de-Lazcano-Lobato, Juan M., Lopez-Rubio, Ezequiel
Format: Conference Proceeding
Language:English
Subjects:
Autoencoders
Foreground Segmentation
Image segmentation
Moving Object Detection
Object detection
Probabilistic logic
Proposals
Shape
Training
Video sequences
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Summary:Noise robustness is crucial when approaching a moving detection problem since image noise is easily mistaken for movement. In order to deal with the noise, deep denoising autoencoders are commonly proposed to be applied on image patches with an inherent disadvantage with respect to the segmentation resolution. In this work, a fully convolutional autoencoder-based moving detection model is proposed in order to deal with noise with no patch extraction required. Different autoencoder structures and training strategies are also tested to get insights into the best network design approach.
ISSN:2381-8549
DOI:10.1109/ICIP46576.2022.9897305