Learning AAM fitting through simulation

Saragih, Jason; Göcke, Roland

doi:10.1016/j.patcog.2009.04.014

Cited by 61 publications

(60 citation statements)

References 29 publications

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“…solving a non-linear optimization problem. The reported convergence rate of this method is 98% (Saragih and Göcke, 2009). Using multi-view AAM registration and estimated observer's 3D position, we determine the observer's head orientation and consequently, if the head is oriented towards the display, this denotes the person's attention.…”

Section: Attention Timementioning

confidence: 91%

Audience Measurement of Digital Signage: Quantitative Study in Real-World Environment Using Computer Vision

Ravnik

Solina

2013

Interacting with Computers

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We present a quantitative study of digital signage audience measurement using computer vision. We developed a camera-enhanced digital signage display that acquires audience measurement metrics with computer vision algorithms. Temporal metrics of a person's dwell time, display in-view time and attention time are extracted. The system also determines demographic metrics of the gender and age group. The digital signage display was deployed in a real-world environment of a clothing boutique, where demographic and viewership data of 1294 store customers were recorded, manually verified and analysed. The analysis shows that 35% of customers specifically looked-at the display, having the average attention time of 0.7 s. Interestingly, the attention time was substantially higher for men (1.2 s) than for women (0.4 s). Age group comparison reveals that children (1-14 years) are the most responsive to the digital signage. Finally, the analysis shows that the average attention time is significantly higher when displaying the dynamic content (0.9 s) when compared with the static content (0.6 s).

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Section: Attention Timementioning

confidence: 91%

Audience Measurement of Digital Signage: Quantitative Study in Real-World Environment Using Computer Vision

Ravnik

Solina

2013

Interacting with Computers

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“…Several approaches Hou et al 2001;Matthews and Baker 2004;Batur and Hayes 2005;Gross et al 2005;Donner et al 2006;Papandreou and Maragos 2008;Liu 2009;Saragih and Göcke 2009;Amberg et al 2009;Tresadern et al 2010;Martins et al 2010;Sauer et al 2011;Tzimiropoulos and Pantic 2013;Kossaifi et al 2014;Antonakos et al 2014) have been proposed to define and solve the previous optimization problem. Broadly speaking, they can be divided into two different groups:…”

Section: Introductionmentioning

confidence: 99%

“…-Regression based Hou et al 2001;Batur and Hayes 2005;Donner et al 2006;Saragih and Göcke 2009;Tresadern et al 2010;Sauer et al 2011) -Optimization based (Matthews and Baker 2004;Gross et al 2005;Papandreou and Maragos 2008;Amberg et al 2009;Martins et al 2010;Tzimiropoulos and Pantic 2013;Kossaifi et al 2014) Regression based techniques attempt to solve the problem by learning a direct function mapping between the error measure and the optimal values of the parameters. Most notable approaches include variations on the original ) fixed linear regression approach of Hou et al (2001), Donner et al (2006), the adaptive linear regression approach of Batur and Hayes (2005), and the works of Saragih and Göcke (2009) and Tresadern et al (2010) which considerably improved upon previous techniques by using boosted regression.…”

Section: Introductionmentioning

confidence: 99%

“…Most notable approaches include variations on the original ) fixed linear regression approach of Hou et al (2001), Donner et al (2006), the adaptive linear regression approach of Batur and Hayes (2005), and the works of Saragih and Göcke (2009) and Tresadern et al (2010) which considerably improved upon previous techniques by using boosted regression. Also, Cootes and Taylor (2001) and Tresadern et al (2010) showed that the use of non-linear gradient-based and Haar-like appearance representations, respectively, lead to better fitting accuracy in regression based AAMs.…”

Section: Introductionmentioning

confidence: 99%

“…However, recent works in this area (Papandreou and Maragos 2008;Saragih and Göcke 2009;Tresadern et al 2010;Lucey et al 2013;Tzimiropoulos and Pantic 2013;Antonakos et al 2014) suggest that these limitations might have been over-stressed in the literature and that AAMs can produce highly accurate results if appropriate training data (Tzimiropoulos and Pantic 2013), appearance representations (Tresadern et al 2010;Lucey et al 2013;Antonakos et al 2014) and fitting strategies (Papandreou and Maragos 2008;Saragih and Göcke 2009;Tresadern et al 2010;Tzimiropoulos and Pantic 2013) are employed.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Unified Framework for Compositional Fitting of Active Appearance Models

Alabort-i-Medina

Zafeiriou

2016

Int J Comput Vis

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Active appearance models (AAMs) are one of the most popular and well-established techniques for modeling deformable objects in computer vision. In this paper, we study the problem of fitting AAMs using compositional gradient descent (CGD) algorithms. We present a unified and complete view of these algorithms and classify them with respect to three main characteristics: (i) cost function; (ii) type of composition; and (iii) optimization method. Furthermore, we extend the previous view by: (a) proposing a novel Bayesian cost function that can be interpreted as a general probabilistic formulation of the well-known project-out loss; (b) introducing two new types of composition, asymmetric and bidirectional, that combine the gradients of both image and appearance model to derive better convergent and more robust CGD algorithms; and (c) providing new valuable insights into existent CGD algorithms by reinterpreting them as direct applications of the Schur complement and the Wiberg method. Finally, in order to encourage open research and facilitate future comparisons with our work, we make the implementation of the algorithms studied in this paper publicly available as part of the Menpo Project (http://www. menpo.org).

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