End-to-end video background subtraction with 3d convolutional neural networks

Sakkos, Dimitrios; Liu, Heng; Han, Jungong; Shao, Ling

doi:10.1007/s11042-017-5460-9

Cited by 102 publications

(75 citation statements)

References 62 publications

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“…Sakkos et al [161] designed an end-to-end 3D-CNN to track temporal changes in video sequences avoiding the use of a background model for the training. 3D-CNN can handle multiple scenes without further fine-tuning on each scene individually.…”

Section: D-cnnsmentioning

confidence: 99%

Deep neural network concepts for background subtraction:A systematic review and comparative evaluation

et al. 2019

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Conventional neural networks show a powerful framework for background subtraction in video acquired by static cameras. Indeed, the well-known SOBS method and its variants based on neural networks were the leader methods on the largescale CDnet 2012 dataset during a long time. Recently, convolutional neural networks which belong to deep learning methods were employed with success for background initialization, foreground detection and deep learned features. Currently, the top current background subtraction methods in CDnet 2014 are based on deep neural networks with a large gap of performance in comparison on the conventional unsupervised approaches based on multi-features or multi-cues strategies. Furthermore, a huge amount of papers was published since 2016 when Braham and Van Droogenbroeck published their first work on CNN applied to background subtraction providing a regular gain of performance. In this context, we provide the first review of deep neural network concepts in background subtraction for novices and experts in order to analyze this success and to provide further directions. For this, we first surveyed the methods used background initialization, background subtraction and deep learned features. Then, we discuss the adequacy of deep neural networks for background subtraction. Finally, experimental results are presented on the CDnet 2014 dataset.

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Section: D-cnnsmentioning

confidence: 99%

Deep neural network concepts for background subtraction:A systematic review and comparative evaluation

et al. 2019

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“…Other authors emulated background subtraction strategies based on DNNs framework [10][11][12]23]. DNNs exhibit excellent performances in segmentation tasks such as semantic segmentation tasks [22,24,25] and foreground segmentation tasks [26].…”

Section: Related Workmentioning

confidence: 99%

“…DNN-based background subtraction methods have different frameworks [22,24,25]. DNNs in [22,24,25] used a single image as an input, whereas the DNN-based background subtraction in [10][11][12]23] used more than two images consisting of observed images and a background image.…”

Section: Related Workmentioning

confidence: 99%

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Analytics of Deep Neural Network-Based Background Subtraction

et al. 2018

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Deep neural network-based (DNN-based) background subtraction has demonstrated excellent performance for moving object detection. The DNN-based background subtraction automatically learns the background features from training images and outperforms conventional background modeling based on handcraft features. However, previous works fail to detail why DNNs work well for change detection. This discussion helps to understand the potential of DNNs in background subtraction and to improve DNNs. In this paper, we observe feature maps in all layers of a DNN used in our investigation directly. The DNN provides feature maps with the same resolution as that of the input image. These feature maps help to analyze DNN behaviors because feature maps and the input image can be simultaneously compared. Furthermore, we analyzed important filters for the detection accuracy by removing specific filters from the trained DNN. From the experiments, we found that the DNN consists of subtraction operations in convolutional layers and thresholding operations in bias layers and scene-specific filters are generated to suppress false positives from dynamic backgrounds. In addition, we discuss the characteristics and issues of the DNN based on our observation.

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“…Another typical preprocessing is to subtract the background to extract the foreground objects. In BEnd-to-end video background subtraction with 3d convolutional neural networks^ [7], Sakkos et al propose an end-to-end temporalaware background subtraction approach with 3D convolutional neural networks. By performing 3D convolutions on the 10 most recent frames of the video, the changes on the spatial and temporal dimension are simultaneously tracked.…”

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confidence: 99%

Guest Editorial: Large-scale 3D Multimedia Analysis and Applications

Zhao

Zhen

2018

Multimed Tools Appl

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With the rapid advancement of networks, graphics hardware, and computing techniques, the 3D information has been widely applied in various domains, such as virtual reality and medical industry. The proliferation of such applications has produced explosively growing 3D multimedia data, which lead to the requirement for large-scale 3D data analysis. The traditional 3D feature representation and learning algorithms may not work well for large-scale analysis, such as 3D retrieval and recognition, since the computational cost in such cases is much larger. How to design efficient and effective 3D feature representation and learning techniques to deal with the 3D big data is desirable and meaningful. This special issue targets the most recent technical progress on the analysis and applications of large-scale 3D multimedia.Submissions came from an open call for papers and with the assistance of professional referees. Eight papers are finally selected out from in total 24 submissions after two rounds of rigorous reviews. These accepted papers cover several popular topics of large-scale 3D multimedia analysis and applications, including 3D recognition, retrieval, compression, preprocessing, etc. We summarize these papers as follows:In B3D skeleton based action recognition by video-domain translation-scale invariant mapping and multi-scale dilated CNN^[4], Li et al. propose an action recognition approach from 3D skeleton videos. By a video domain translation-scale invariant image mapping, the 3D skeleton videos are transformed to skeleton color images, which are input to a multi-scale Multimed Tools Appl (2018) 77:22897-22900 https://doi

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End-to-end video background subtraction with 3d convolutional neural networks

Cited by 102 publications

References 62 publications

Deep neural network concepts for background subtraction:A systematic review and comparative evaluation

Deep neural network concepts for background subtraction:A systematic review and comparative evaluation

Analytics of Deep Neural Network-Based Background Subtraction

Guest Editorial: Large-scale 3D Multimedia Analysis and Applications

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