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The Effect of Stereoscopic Cues on Multiple Object Tracking in a 3D Virtual Environment
Title
The Effect of Stereoscopic Cues on Multiple Object Tracking in a 3D Virtual Environment.
Creator
Oliveira, Steven Milanez, Barenholtz, Elan, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Psychology
Abstract/Description
Research on Multiple Object Tracking (MOT) has typically involved 2D displays where stimuli move in a single depth plane. However, under natural conditions, objects move in 3D which adds complexity to tracking. According to the spatial interference model, tracked objects have an inhibitory surround that when crossed causes tracking errors. How do these inhibitory fields translate to 3D space? Does multiple object tracking operate on a 2D planar projection, or is it in fact 3D? To investigate...
Show moreResearch on Multiple Object Tracking (MOT) has typically involved 2D displays where stimuli move in a single depth plane. However, under natural conditions, objects move in 3D which adds complexity to tracking. According to the spatial interference model, tracked objects have an inhibitory surround that when crossed causes tracking errors. How do these inhibitory fields translate to 3D space? Does multiple object tracking operate on a 2D planar projection, or is it in fact 3D? To investigate this, we used a fully immersive virtual-reality environment where participants were required to track 1 to 4 moving objects. We compared performance to a condition where participants viewed the same stimuli on a computer screen with monocular depth cues. Results suggest that participants were more accurate in the VR condition than the computer screen condition. This demonstrates interference is negligent when the objects are spatially distant, yet proximate within the 2D projection.
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Date Issued
2017
PURL
http://purl.flvc.org/fau/fd/FA00004943, http://purl.flvc.org/fau/fd/FA00004943
Subject Headings
Pattern perception., Virtual reality., Interactive multimedia., Computer simulation., Computer vision--Mathematical models., Automatic tracking--Mathematical models.
Format
Document (PDF)
Context-based Image Concept Detection and Annotation
Title
Context-based Image Concept Detection and Annotation.
Creator
Zolghadr, Esfandiar, Furht, Borko, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Scene understanding attempts to produce a textual description of visible and latent concepts in an image to describe the real meaning of the scene. Concepts are either objects, events or relations depicted in an image. To recognize concepts, the decision of object detection algorithm must be further enhanced from visual similarity to semantical compatibility. Semantically relevant concepts convey the most consistent meaning of the scene. Object detectors analyze visual properties (e.g., pixel...
Show moreScene understanding attempts to produce a textual description of visible and latent concepts in an image to describe the real meaning of the scene. Concepts are either objects, events or relations depicted in an image. To recognize concepts, the decision of object detection algorithm must be further enhanced from visual similarity to semantical compatibility. Semantically relevant concepts convey the most consistent meaning of the scene. Object detectors analyze visual properties (e.g., pixel intensities, texture, color gradient) of sub-regions of an image to identify objects. The initially assigned objects names must be further examined to ensure they are compatible with each other and the scene. By enforcing inter-object dependencies (e.g., co-occurrence, spatial and semantical priors) and object to scene constraints as background information, a concept classifier predicts the most semantically consistent set of names for discovered objects. The additional background information that describes concepts is called context. In this dissertation, a framework for building context-based concept detection is presented that uses a combination of multiple contextual relationships to refine the result of underlying feature-based object detectors to produce most semantically compatible concepts. In addition to the lack of ability to capture semantical dependencies, object detectors suffer from high dimensionality of feature space that impairs them. Variances in the image (i.e., quality, pose, articulation, illumination, and occlusion) can also result in low-quality visual features that impact the accuracy of detected concepts. The object detectors used to build context-based framework experiments in this study are based on the state-of-the-art generative and discriminative graphical models. The relationships between model variables can be easily described using graphical models and the dependencies and precisely characterized using these representations. The generative context-based implementations are extensions of Latent Dirichlet Allocation, a leading topic modeling approach that is very effective in reduction of the dimensionality of the data. The discriminative contextbased approach extends Conditional Random Fields which allows efficient and precise construction of model by specifying and including only cases that are related and influence it. The dataset used for training and evaluation is MIT SUN397. The result of the experiments shows overall 15% increase in accuracy in annotation and 31% improvement in semantical saliency of the annotated concepts.
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Date Issued
2016
PURL
http://purl.flvc.org/fau/fd/FA00004745, http://purl.flvc.org/fau/fd/FA00004745
Subject Headings
Computer vision--Mathematical models., Pattern recognition systems., Information visualization., Natural language processing (Computer science), Multimodal user interfaces (Computer systems), Latent structure analysis., Expert systems (Computer science)
Format
Document (PDF)