Foreground detection by probabilistic modeling of the features discovered by stacked denoising autoencoders in noisy video sequences

García‐González, Jorge; Ortiz-de-Lazcano-Lobato, Juan Miguel; Luque‐Baena, Rafael Marcos; Molina‐Cabello, Miguel A.; López‐Rubio, Ezequiel

doi:10.1016/j.patrec.2019.06.006

Cited by 18 publications

(5 citation statements)

References 19 publications

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“…The network is first initialized with greedy layer wise pretraining approach and then fine tuned using conjugate gradient based back propagation algorithm. Garcia et al [175], [176] proposed a CD system with a stacked denoising autoencoder extracting the salient features for each patch of several shifted tilings of the video frame. For each patch of the frame, a probabilistic model is learned that are considered in pixellevel classification.…”

Section: D-cnnmentioning

confidence: 99%

An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs

Mandal¹,

Vipparthi²

2021

Preprint

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Visual change detection, aiming at segmentation of video frames into foreground and background regions, is one of the elementary tasks in computer vision and video analytics. The applications of change detection include anomaly detection, object tracking, traffic monitoring, human machine interaction, behavior analysis, action recognition, and visual surveillance. Some of the challenges in change detection include background fluctuations, illumination variation, weather changes, intermittent object motion, shadow, fast/slow object motion, camera motion, heterogeneous object shapes and realtime processing. Traditionally, this problem has been solved using hand-crafted features and background modelling techniques. In recent years, deep learning frameworks have been successfully adopted for robust change detection. This article aims to provide an empirical review of the state-of-the-art deep learning methods for change detection. More specifically, we present a detailed analysis of the technical characteristics of different model designs and experimental frameworks. We provide model design based categorization of the existing approaches, including the 2D-CNN, 3D-CNN, ConvLSTM, multi-scale features, residual connections, autoencoders and GAN based methods. Moreover, an empirical analysis of the evaluation settings adopted by the existing deep learning methods is presented. To the best of our knowledge, this is a first attempt to comparatively analyze the different evaluation frameworks used in the existing deep change detection methods. Finally, we point out the research needs, future directions and draw our own conclusions.

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Section: D-cnnmentioning

confidence: 99%

An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs

Mandal¹,

Vipparthi²

2021

Preprint

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“…These methods do not successfully adapt to complex scenarios in MOD. Inspired by the success of deep CNNs on a wide variety of visual recognition tasks [42], several studies have also been proposed to handle the MOD problem in a fully supervised manner [15,8]. However, these deep learning methods usually re-quire a large amount of training data.…”

Section: Related Workmentioning

confidence: 99%

Graph CNN for Moving Object Detection in Complex Environments from Unseen Videos

Giraldo

Javed

Werghi

et al. 2021

2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)

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Moving Object Detection (MOD) is a fundamental step for many computer vision applications. MOD becomes very challenging when a video sequence captured from a static or moving camera suffers from the challenges: camouflage, shadow, dynamic backgrounds, and lighting variations, to name a few. Deep learning methods have been successfully applied to address MOD with competitive performance. However, in order to handle the overfitting problem, deep learning methods require a large amount of labeled data which is a laborious task as exhaustive annotations are always not available. Moreover, some MOD deep learning methods show performance degradation in the presence of unseen video sequences because the testing and training splits of the same sequences are involved during the network learning process. In this work, we pose the problem of MOD as a node classification problem using Graph Convolutional Neural Networks (GCNNs). Our algorithm, dubbed as GraphMOD-Net, encompasses instance segmentation, background initialization, feature extraction, and graph construction. GraphMOD-Net is tested on unseen videos and outperforms state-of-the-art methods in unsupervised, semi-supervised, and supervised learning in several challenges of the Change Detection 2014 (CDNet2014) and UCSD background subtraction datasets.

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“…The network takes a red–blue–green (RGB) image in three different scales and generates a foreground segmentation probability mask for the corresponding image. In the period of 2018–2019, numerous deep learning models either based on auto‐encoder [29–31] and CNNs [32–36] have been proposed. However, all these methods are supervised and have been trained on ground truth video frames of datasets and tested on the same types of videos.…”

Section: Related Workmentioning

confidence: 99%

Deep detector classifier (DeepDC) for moving objects segmentation and classification in video surveillance

Ammar

Bouwmans

Zaghden

et al. 2020

IET Image Processing

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Foreground detection by probabilistic modeling of the features discovered by stacked denoising autoencoders in noisy video sequences

Cited by 18 publications

References 19 publications

An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs

An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs

Graph CNN for Moving Object Detection in Complex Environments from Unseen Videos

Deep detector classifier (DeepDC) for moving objects segmentation and classification in video surveillance

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