Evaluation of background subtraction techniques for video surveillance

Brutzer, Sebastian; Höferlin, Benjamin; Heidemann, Gunther

doi:10.1109/cvpr.2011.5995508

Cited by 514 publications

(358 citation statements)

References 28 publications

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“…It is thus recommended to include postprocessing operations, even when comparing techniques. This was also the conclusion of Brutzer et al [24] and Benezeth et al [14]. Note that other filters can be used such as temporal filters, shadow filters [24], and complex spatio-temporal filtering techniques to relabel the classification results.…”

Section: Spatial Aggregation Markovian Models and Post-processingmentioning

confidence: 69%

Background Subtraction on Embedded Hardware

Jodoin¹,

Pierard

Wang³

et al. 2014

Background Modeling and Foreground Detection for Video Surveillance

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Section: Spatial Aggregation Markovian Models and Post-processingmentioning

confidence: 69%

Background Subtraction on Embedded Hardware

Jodoin¹,

Pierard

Wang³

et al. 2014

Background Modeling and Foreground Detection for Video Surveillance

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“…The average processing time on a sequence of 100 RGB frames with resolution 600 × 800 with image alignment and background motion estimation is about 665 seconds, which is decreased to 195 seconds with CSSP, meaning a time-saving of more than 3.4 times. We perform extensive tests using four datasets [20], [21], [22], [23], comprised of 49 videos with various challenges. This allows us to compare our method to a large number of alternative methods.…”

Section: Resultsmentioning

confidence: 99%

Dynamic tree-structured sparse RPCA via column subset selection for background modeling and foreground detection

Ebadi

Ones

Izquierdo

2016

2016 IEEE International Conference on Image Processing (ICIP)

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Video analysis often begins with background subtraction, which consists of creation of a background model that allows distinguishing foreground pixels. Recent evaluation of background subtraction techniques demonstrated that there are still considerable challenges facing these methods. Processing per-pixel basis from the background is not only time-consuming but also can dramatically affect foreground region detection, if region cohesion and contiguity is not considered in the model. We present a new method in which we regard the image sequence to be made up of the sum of a low-rank background matrix and a dynamic tree-structured sparse matrix, and solve the decomposition using our approximated Robust Principal Component Analysis method extended to handle camera motion. Furthermore, to reduce the curse of dimensionality and scale, we introduce a low-rank background modeling via Column Subset Selection that reduces the order of complexity, decreases computation time, and eliminates the huge storage need for large videos.

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“…Both techniques can also be combined. Note that post-processing techniques are known to always increase the performance [3,12]. In this paper, we study the performance of the BGS without any post-processing.…”

Section: The Traditional Processing Pipeline Of Bgs Algorithmsmentioning

confidence: 99%

A Perfect Estimation of a Background Image Does Not Lead to a Perfect Background Subtraction: Analysis of the Upper Bound on the Performance

Pierard

Droogenbroeck

2015

New Trends in Image Analysis and Processing -- ICIAP 2015 Workshops

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Abstract. The quest for the best background subtraction technique is ongoing. Despite that a considerable e ort has been undertaken to develop exible and e cient methods, some elementary questions are still unanswered. One of them is the existence of an intrinsic upper bound to the performance. In fact, data are a ected by noise, and therefore it is illusory to believe that it is possible to achieve a perfect segmentation.This paper aims at exploring some intrinsic limitations of the principle of background subtraction. The purpose consists in studying the impact of several limiting factors separately. One of our conclusions is that even if an algorithm would be able to calculate a perfect background image, it is not su cient to achieve a perfect segmentation with background subtraction, due to other intrinsic limitations.

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Evaluation of background subtraction techniques for video surveillance

Cited by 514 publications

References 28 publications

Background Subtraction on Embedded Hardware

Background Subtraction on Embedded Hardware

Dynamic tree-structured sparse RPCA via column subset selection for background modeling and foreground detection

A Perfect Estimation of a Background Image Does Not Lead to a Perfect Background Subtraction: Analysis of the Upper Bound on the Performance

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