A benchmark for Background Subtraction Algorithms in monocular vision: A comparative study

Dhome, Yoann; Tronson, Nicolas; Vacavant, Antoine; Chateau, Thierry; Gabard, Christophe; Goyat, Yann; Gruyer, Dominique

doi:10.1109/ipta.2010.5586792

Cited by 23 publications

(16 citation statements)

References 11 publications

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“…We compared our algorithm to Chen et al [6] which has been proved to be a very competitive state of the art method [9], and to Stauffer and Grimson [13]. In a first part evaluation is done on several sequences acquired during a guard tour over an area of 40 × 40 meters square.…”

Section: Resultsmentioning

confidence: 99%

Background subtraction adapted to PTZ cameras by keypoint density estimation

Guillot

Taron

Sayd

et al. 2010

Procedings of the British Machine Vision Conference 2010

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PTZ cameras have the ability to cover wide areas with an adapted resolution. In this article we propose a background subtraction algorithm suitable for a PTZ camera performing a guard tour. It relies on the estimation of a probability density function based on the matching of keypoints between the background and the current image. Its main interest consists in the resulting robustness to sudden illumination changes. Experiments show that our algorithm is more robust to sudden changes in illumination than one of the state of the art approaches in situations of low camera frame-rate. These properties allow us to successfully maintain up to date the background model of a wide scene made up of a collection of images.

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Section: Resultsmentioning

confidence: 99%

Background subtraction adapted to PTZ cameras by keypoint density estimation

Guillot

Taron

Sayd

et al. 2010

Procedings of the British Machine Vision Conference 2010

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“…Meanwhile, we choose the F1measure to evaluate the performance of moving object detection task [33]. Besides, we consider the moving object detection problem as a binary classification scheme [34]. Then, positives and negatives are counted at the pixel level.…”

Section: Weighting Parameter Adaptationmentioning

confidence: 99%

Video analytical coding: When video coding meets video analysis

Liu

Zhu

Mao

et al. 2018

Signal Processing: Image Communication

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Leveraging on the properties of human visual system, most of the well-designed video coding standards utilize rate-distortion optimization techniques by maximizing a fidelity cost function (e.g. peak signal noise ratio, PSNR) under an available bit rate budget constrain. However, a huge amount of video data is consumed by computers rather than by human beings in several application scenarios. In view of this, this paper proposes a new coding framework called video analytical coding (VAC) for video analysis. We use the term "analytical distortion" to denote the difference of video analysis performance when video quality degrades and analytical distortion is estimated by compression distortion. Meanwhile, we develop a new rate-analytical-distortion optimization (RADO) method to trade off the bit rate and the analytical distortion. Specifically, we consider moving object detection as the analysis task and develop a novel rate analytical distortion (RAD) model and a quantization parameter adaptation strategy for video coding, where the analytical distortion is related to the object detection performance represented as F1measure. Experimental results show that the performance of the video analysis task can be significantly improved (up to 40% reduction of analytical distortion).

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“…Bouwmans 10 Mixture of Gaussians (MoG) 15 Mixture of Gaussians with particle swarm optimization (MoG-PSO) 100 Improved MoG 101 MoG with MRF 102 MoG improved HLS color space 103 Spatial-Time adaptive per pixel mixture of Gaussian (S-TAP-MoG) 104 Adaptive spatio-temporal neighborhood analysis (ASTNA) 105 Subspace learning-principle component analysis (Eigen-Backgrounds) 42 Subspace learning independent component analysis (SL-ICA) 106 Subspace learning incremental non-negative matrix factorization (SL-INMF) 107 Subspace learning using incremental ranktensor (SL-IRT) 108 Wallflower Bayesian multi-layer 36 Histogram over time 13 Local-self similarity 110 Bianco et al 130 IUTIS-1 130 IUTIS-2 130 IUTIS-3 130 Flux tensor with split Gaussian models (FTSG) 124 Self-balanced local sensitivity (SuBSENSE) 123 Weightless neural networks (CwisarDH) 51,122 Spectral-360 121 fast self-tuning BS 120 K-nearest neighbor method (KNN) 119 Kernel density estimation(KDE) 16 Spatially coherent self-organized background subtraction (SC-SOBS) 22 Fish4knowledge § §143 for underwater fish detection and tracking. Other comparative studies can be found in [144][145][146][147][148] .…”

Section: Recall Precisionmentioning

confidence: 99%

Comparative study of motion detection methods for video surveillance systems

Sehairi

Chouireb

Meunier

2017

J. Electron. Imaging

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The objective of this study is to compare several change detection methods for a mono static camera and identify the best method for different complex environments and backgrounds in indoor and outdoor scenes. To this end, we used the CDnet video dataset * as a benchmark that consists of many challenging problems, ranging from basic simple scenes to complex scenes affected by bad weather and dynamic backgrounds. Twelve change detection methods, ranging from simple temporal differencing to more sophisticated methods, were tested and several performance metrics were used to precisely evaluate the results. Because most of the considered methods have not previously been evaluated on this recent large scale dataset, this work compares these methods to fill a lack in the literature, and thus this evaluation joins as complementary compared with the previous comparative evaluations. Our experimental results show that there is no perfect method for all challenging cases; each method performs well in certain cases and fails in others. However, this study enables the user to identify the most suitable method for his or her needs.

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A benchmark for Background Subtraction Algorithms in monocular vision: A comparative study

Cited by 23 publications

References 11 publications

Background subtraction adapted to PTZ cameras by keypoint density estimation

Background subtraction adapted to PTZ cameras by keypoint density estimation

Video analytical coding: When video coding meets video analysis

Comparative study of motion detection methods for video surveillance systems

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