A Deep Convolutional Neural Network for Background Subtraction

Babaee, Mohammadreza; Dinh, Duc Tung; Rigoll, Gerhard

doi:10.48550/arxiv.1702.01731

Cited by 10 publications

(14 citation statements)

References 22 publications

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“…Since camera traps are static, detecting animals in the images could be considered either a change detection or foreground detection problem. Detecting changes and/or foreground vs. background in video is a well studied problem [36], [37]. Many of these methods rely on constructing a good background model that updates regularly, and thus degrade rapidly at low frame rates.…”

Section: Detectionmentioning

confidence: 99%

Recognition in Terra Incognita

Beery

Horn

Perona

2018

Lecture Notes in Computer Science

430

402

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It is desirable for detection and classification algorithms to generalize to unfamiliar environments, but suitable benchmarks for quantitatively studying this phenomenon are not yet available. We present a dataset designed to measure recognition generalization to novel environments. The images in our dataset are harvested from twenty camera traps deployed to monitor animal populations. Camera traps are fixed at one location, hence the background changes little across images; capture is triggered automatically, hence there is no human bias. The challenge is learning recognition in a handful of locations, and generalizing animal detection and classification to new locations where no training data is available. In our experiments state-of-the-art algorithms show excellent performance when tested at the same location where they were trained. However, we find that generalization to new locations is poor, especially for classification systems. 1

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Section: Detectionmentioning

confidence: 99%

Recognition in Terra Incognita

Beery

Horn

Perona

2018

Lecture Notes in Computer Science

430

402

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“…This method achieved an average F-Measure of 0.9046 † † The values are obtained from their paper. [13] trained their model all at once by combining training frames from various video sequences; in particular, including 5% of frames from each video sequence. They followed the same training procedure as in [12], in which image-patches were combined with background-patches then fed to the network.…”

Section: Related Workmentioning

confidence: 99%

“…A common method for segmenting moving objects in a scene is to perform a background subtraction, in which moving objects are considered as foreground pixels and non-moving objects are considered as background pixels. This binary classification problem has been extensively studied and improved over the years, and several approaches have been proposed concurrently [7][8][9][10][11][12][13][14][15]. There are many challenges in developing a robust background subtraction algorithm: sudden or gradual illumination changes, shadows cast by foreground objects, dynamic background motion (waving tree, rain, snow, air turbulence), camera motion (camera jittering, camera panning-tilting-zooming), camouflage or subtle regions, i.e.…”

Section: Introductionmentioning

confidence: 99%

Foreground segmentation using convolutional neural networks for multiscale feature encoding

Lim

Keleş

2018

Pattern Recognition Letters

251

193

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A common approach for moving objects segmentation in a scene is to perform a background subtraction. Several methods have been proposed in this domain. However, they lack the ability of handling various difficult scenarios such as illumination changes, background or camera motion, camouflage effect, shadow etc. To address these issues, we propose a robust and flexible encoder-decoder type neural network based approach. We adapt a pre-trained convolutional network, i.e. VGG-16 Net, under a triplet framework in the encoder part to embed an image in multiple scales into the feature space and use a transposed convolutional network in the decoder part to learn a mapping from feature space to image space. We train this network end-to-end by using only a few training samples. Our network takes an RGB image in three different scales and produces a foreground segmentation probability mask for the corresponding image. In order to evaluate our model, we entered the Change Detection 2014 Challenge (changedetection.net) and our method outperformed all the existing state-of-the-art methods by an average F-Measure of 0.9770. Our source code will be made publicly available at https://github.com/limanggun/FgSegNet.

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“…Recently, both CNN and foreground attentive neural network (FANN) models have been developed to perform foreground segmentation [62], [63]. In addition to conventional Gaussian mixture model-based background subtraction, recent explorations have also shown that CNN models could be effectively used for the same purpose [64], [65]. To address these separated foreground objects and background attributes, Zhang et al [66] introduced a new background mode to more compactly represent background information with better R-D efficiency.…”

Section: Overview Of Dnn-based Video Pre-processingmentioning

confidence: 99%

Advances In Video Compression System Using Deep Neural Network: A Review And Case Studies

Ding¹,

Ma²,

Chen³

et al. 2021

Preprint

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Significant advances in video compression system have been made in the past several decades to satisfy the nearly exponential growth of Internet-scale video traffic. From the application perspective, we have identified three major functional blocks including pre-processing, coding, and postprocessing, that have been continuously investigated to maximize the end-user quality of experience (QoE) under a limited bit rate budget. Recently, artificial intelligence (AI) powered techniques have shown great potential to further increase the efficiency of the aforementioned functional blocks, both individually and jointly. In this article, we review extensively recent technical advances in video compression system, with an emphasis on deep neural network (DNN)-based approaches; and then present three comprehensive case studies. On pre-processing, we show a switchable texture-based video coding example that leverages DNN-based scene understanding to extract semantic areas for the improvement of subsequent video coder. On coding, we present an end-to-end neural video coding framework that takes advantage of the stacked DNNs to efficiently and compactly code input raw videos via fully data-driven learning. On post-processing, we demonstrate two neural adaptive filters to respectively facilitate the in-loop and post filtering for the enhancement of compressed frames. Finally, a companion website hosting the contents developed in this work can be accessed publicly at https://purdueviper.github.io/dnn-coding/.

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A Deep Convolutional Neural Network for Background Subtraction

Cited by 10 publications

References 22 publications

Recognition in Terra Incognita

Recognition in Terra Incognita

Foreground segmentation using convolutional neural networks for multiscale feature encoding

Advances In Video Compression System Using Deep Neural Network: A Review And Case Studies

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