Background Subtraction

By Chris Stauffer, W. Grimson. Encyclopedia of Biometrics, 2009

5,222 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/4215f8bdb3bef14fb698e31e56959a0f02d4c7f9

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Citations in the corpus

Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering (2016, methodology): “ optimized by the Expectation Maximization (EM) algorithm. One advantage of GMM over K-means is that a GMM can generate samples by estimation of data density. Although K-means, GMM and their variants [33,27,25,38,15,8] have been extensively used, learning good representations most suitable for clust”
ViBe: A Universal Background Subtraction Algorithm for Video Sequences (2011, methodology): “For the EGMM algorithm of [50], we used the implementation available on the author’s website1.”
Changedetection.net: A new change detection benchmark dataset (2012, background): “As a consequence, an overwhelming importance has been accorded to a small subset of easily implementable methods such as [23, 9, 26] that were developed in the late 1990’s.”
Semantic event representation and recognition using syntactic attribute graph grammar (2009, background): “To achieve this task, a number of key issues, such as background modeling (16), object tracking (12), object detection/classification (11; 19; 10), illumination/occlussion problems (8), are well studied in computer vision research.”
A hand pose tracking benchmark from stereo matching (2017, methodology): “B5 in Fig.4 (b) shows that the hand probability from the proposed method is relatively high in the background since its background also has fast motion objects and adaptive GMM treats them as foreground.”
Understanding Transit Scenes: A Survey on Human Behavior-Recognition Algorithms (2010, methodology): “Common algorithms include methods by Heikkila and Silven [40], Stauffer and Grimson (adaptive Gaussian mixture model or GMM) [41], Halevy and Weinshall [42], Cutler and Davis [43], and Toyama et al. (Wallflower) [44].”
Maximum Margin Criterion (2018, background): “Recently, White et al. [1] proved that the Gaussian Mixture Model (GMM) [19] gives better results when some coefficients are determined in a supervised way.”
A Review of Computer Vision Techniques for the Analysis of Urban Traffic (2011, background): “An overview and grouping by the complexity of systems is provided in Table I.”
A brief introduction to OpenCV (2012, background): “for each frame a background is updated) rather than to work with the same background image appearance throughout the process [21].”
A probabilistic approach for damage identification and crack mode classification in reinforced concrete structures (2013, methodology): “, 2006), dynamical systems and tracking (Povinelli et al., 2004; Stauffer and Grimson, 1999; Terejanu et al., 2008), and text recognition (Richiardi and Drygajlo, 2003); however, the use of this technique for AE-based structural health monitoring (SHM) has not been investigated.”
Identifying Basketball Plays from Sensor Data; Towards a Low-Cost Automatic Extraction of Advanced Statistics (2017, methodology): “Then, the phase of the game (offensive / defensive / time-out) is found by clustering the distribution of players on court with a Gaussian Mixture Model [15].”
A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications (2008, background): “The main tasks in visual surveillance systems include motion detection, object classification, tracking, activity understanding, Manuscript received November 13, 2007; revised March 26, 2008.”