Moving Target Selection in 2D Graphical User Interfaces

Hajri, Abir Al; Fels, Sidney; Miller, Gregor; Ilich, Michael

doi:10.1007/978-3-642-23771-3_12

Cited by 32 publications

(24 citation statements)

References 27 publications

(41 reference statements)

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“…Beyond size, distance and movement, this approach could be extended to consider other factors such as target semantics, if any, as well as user behaviors and gestures. The potential of using other "indices of difficulty," formulated specifically for moving-target selection [1,9,10], to predict user intention should also be explored. Finally, it should also be possible to refine decision trees in real time, to adapt the generated models to each user.…”

Section: Discussionmentioning

confidence: 99%

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Towards a Model for Predicting Intention in 3D Moving-Target Selection Tasks

Casallas

Oliver

Kelly

et al. 2013

Engineering Psychology and Cognitive Ergonomics. Understanding Human Cognition

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Novel interaction techniques have been developed to address the difficulties of selecting moving targets. However, similar to their static-target counterparts, these techniques may suffer from clutter and overlap, which can be addressed by predicting intended targets. Unfortunately, current predictive techniques are tailored towards static-target selection. Thus, a novel approach for predicting user intention in movingtarget selection tasks using decision-trees constructed with the initial physical states of both the user and the targets is proposed. This approach is verified in a virtual reality application in which users must choose, and select between different moving targets. With two targets, this model is able to predict user choice with approximately 71% accuracy, which is significantly better than both chance and a frequentist approach. KeywordsDepartment of Psychology, Virtual Reality Application Center, User intention, prediction, Fitts' Law, movingtarget selection, perceived difficulty, decision trees, virtual reality Abstract. Novel interaction techniques have been developed to address the difficulties of selecting moving targets. However, similar to their static-target counterparts, these techniques may suffer from clutter and overlap, which can be addressed by predicting intended targets. Unfortunately, current predictive techniques are tailored towards static-target selection. Thus, a novel approach for predicting user intention in movingtarget selection tasks using decision-trees constructed with the initial physical states of both the user and the targets is proposed. This approach is verified in a virtual reality application in which users must choose, and select between different moving targets. With two targets, this model is able to predict user choice with approximately 71% accuracy, which is significantly better than both chance and a frequentist approach.

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Section: Discussionmentioning

confidence: 99%

“…Unfortunately, most of the HCI studies on selection, based on Fitts' Law [4], have focused on static targets (for a compendium, see, for example [6]). Recently, however, new performance models [1] and interaction techniques [8] have been proposed to address the specificities and difficulties of moving-target selection.…”

Section: Introductionmentioning

confidence: 99%

Towards a Model for Predicting Intention in 3D Moving-Target Selection Tasks

Casallas

Oliver

Kelly

et al. 2013

Engineering Psychology and Cognitive Ergonomics. Understanding Human Cognition

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“…In order to both provide TLD with hints to an object's initial position on a live video feed, and to select the objects' anchors, we needed to devise a way to handle the selection of moving objects. Two variants of the Hold method [11] were used to this end.…”

Section: Object Trackingmentioning

confidence: 99%

Real-time annotation of video objects on tablet computers

Silva

Cabral

Fernandes

et al. 2012

Proceedings of the 11th International Conference on Mobile and Ubiquitous Multimedia

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When using a tablet computer, sketching is a natural approach for users to annotate video scenes. However, when these annotations are done in real-time and overlaid in the video, their context can be lost due to changes in the scene being annotated. We propose an approach towards maintaining the annotations' context, by using object tracking to create anchors onto which further annotations can be attached. To this end, the annotator is capable of using different tracking methods, including a Kinect sensor and/or the TLD object tracking algorithm.The challenges involved in designing an interface to support the association of video annotations with tracked objects in real-time are also discussed. In particular, we discuss our alternative approaches to handle moving object selection on live video, which we have called "Hold and Overlay" and "Hold and Speed Up". In addition, the results of a set of preliminary tests are reported.

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“…1 The rotate( V , Q) function was implemented in R, based on OpenSceneGraph's osg :: Quat:: operator * (osg :: Vec3) method…”

Section: Relative User-target Featuresmentioning

confidence: 99%

“…Recently, however, new performance models [1] and interaction techniques [12,29,41] have been proposed to address the specificities of moving-target selection. Interestingly, these models and interaction techniques are inspired or derived on their static Fitts' counterparts.…”

Section: Introductionmentioning

confidence: 99%

Using relative head and hand-target features to predict intention in 3D moving-target selection

Casallas

Oliver²,

Kelly³

et al. 2014

2014 IEEE Virtual Reality (VR)

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Science Arts & Métiers (SAM)is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. ABSTRACTSelection of moving targets is a common, yet complex task in human-computer interaction (HCI) and virtual reality (VR). Predicting user intention may be beneficial to address the challenges inherent in interaction techniques for moving-target selection. This article extends previous models by integrating relative head-target and hand-target features to predict intended moving targets. The features are calculated in a time window ending at roughly two-thirds of the total target selection time and evaluated using decision trees. With two targets, this model is able to predict user choice with up to ∼ 72% accuracy on general moving-target selection tasks and up to ∼ 78% by also including task-related target properties.

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Moving Target Selection in 2D Graphical User Interfaces

Cited by 32 publications

References 27 publications

Towards a Model for Predicting Intention in 3D Moving-Target Selection Tasks

Towards a Model for Predicting Intention in 3D Moving-Target Selection Tasks

Real-time annotation of video objects on tablet computers

Using relative head and hand-target features to predict intention in 3D moving-target selection

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