Journal ArticleDOI
Deep learning driven blockwise moving object detection with binary scene modeling
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TLDR
A deep learning based block-wise scene analysis method equipped with a binary spatio-temporal scene model based on the stacked denoising autoencoder that ensures the high efficiency of moving object detection.About:
This article is published in Neurocomputing.The article was published on 2015-11-30. It has received 58 citations till now. The article focuses on the topics: Scene statistics & Object detection.read more
Citations
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Journal ArticleDOI
Deep neural network concepts for background subtraction:A systematic review and comparative evaluation
TL;DR: In this article, the authors provide a review of deep neural network concepts in background subtraction for novices and experts in order to analyze this success and to provide further directions.
Journal ArticleDOI
Learning deep event models for crowd anomaly detection
TL;DR: Experimental results show that the deep model is effective for abnormal event detection in video surveillance, and the proposed method acquires competitive performance with relatively few parameters.
Journal ArticleDOI
Background Subtraction in Real Applications: Challenges, Current Models and Future Directions
TL;DR: In this paper, a survey of background subtraction methods used in real applications is presented, in order to identify the real challenges met in practice, the current used background models and to provide future directions.
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Background Subtraction in Real Applications: Challenges, Current Models and Future Directions
TL;DR: This work identifies the background models that are effectively used in real applications that used background subtraction in order to identify the real challenges met in practice, the current used background models and to provide future directions.
Journal ArticleDOI
A 3D CNN-LSTM-Based Image-to-Image Foreground Segmentation
TL;DR: The analysis of experimental results via standard quantitative metrics on 16 benchmark datasets validates that the proposed 3D CNN-LSTM achieves competitive performance in terms of figure of merit evaluated against prior and state-of-the-art methods.
References
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Proceedings ArticleDOI
Adaptive background mixture models for real-time tracking
Chris Stauffer,W.E.L. Grimson +1 more
TL;DR: This paper discusses modeling each pixel as a mixture of Gaussians and using an on-line approximation to update the model, resulting in a stable, real-time outdoor tracker which reliably deals with lighting changes, repetitive motions from clutter, and long-term scene changes.
Journal ArticleDOI
Object tracking: A survey
TL;DR: The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends to discuss the important issues related to tracking including the use of appropriate image features, selection of motion models, and detection of objects.
Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising 1 criterion
TL;DR: This work clearly establishes the value of using a denoising criterion as a tractable unsupervised objective to guide the learning of useful higher level representations.
Journal Article
Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion
TL;DR: Denoising autoencoders as mentioned in this paper are trained locally to denoise corrupted versions of their inputs, which is a straightforward variation on the stacking of ordinary autoencoder.
Book ChapterDOI
A Practical Guide to Training Restricted Boltzmann Machines
TL;DR: This guide is an attempt to share expertise at training restricted Boltzmann machines with other machine learning researchers.