1,241 research outputs found
Improved data association and occlusion handling for vision-based people tracking by mobile robots
This paper presents an approach for tracking multiple persons using a combination of colour and thermal vision sensors on a mobile robot. First, an adaptive colour model is incorporated into the measurement model of the tracker. Second, a new approach for detecting occlusions is introduced, using a machine learning classifier for pairwise comparison of persons (classifying which one is in front of the other). Third, explicit occlusion handling is then incorporated into the tracker
Obscurance-based Volume Rendering Framework
IEEE/ EG Symposium on Volume and Point-Based Graphics (2008) H.- C. Hege, D. Laidlaw, R. Pajarola, O. Staadt (Editors)lighting effects in a faster way than global illumination. Its application in volume visualization is of special interest since it permits us to generate a high quality rendering at a low cost. In this paper, we propose an obscurancebased framework that allows us to obtain realistic and illustrative volume visualizations in an interactive manner. Obscurances can include color bleeding effects without additional cost. Moreover, we obtain a saliency map from the gradient of obscurances and we show its application to enhance volume visualization and to select the most salient views.publishedVersio
Robustness of 3D Deep Learning in an Adversarial Setting
Understanding the spatial arrangement and nature of real-world objects is of
paramount importance to many complex engineering tasks, including autonomous
navigation. Deep learning has revolutionized state-of-the-art performance for
tasks in 3D environments; however, relatively little is known about the
robustness of these approaches in an adversarial setting. The lack of
comprehensive analysis makes it difficult to justify deployment of 3D deep
learning models in real-world, safety-critical applications. In this work, we
develop an algorithm for analysis of pointwise robustness of neural networks
that operate on 3D data. We show that current approaches presented for
understanding the resilience of state-of-the-art models vastly overestimate
their robustness. We then use our algorithm to evaluate an array of
state-of-the-art models in order to demonstrate their vulnerability to
occlusion attacks. We show that, in the worst case, these networks can be
reduced to 0% classification accuracy after the occlusion of at most 6.5% of
the occupied input space.Comment: 10 pages, 8 figures, 1 tabl
SALSA: A Novel Dataset for Multimodal Group Behavior Analysis
Studying free-standing conversational groups (FCGs) in unstructured social
settings (e.g., cocktail party ) is gratifying due to the wealth of information
available at the group (mining social networks) and individual (recognizing
native behavioral and personality traits) levels. However, analyzing social
scenes involving FCGs is also highly challenging due to the difficulty in
extracting behavioral cues such as target locations, their speaking activity
and head/body pose due to crowdedness and presence of extreme occlusions. To
this end, we propose SALSA, a novel dataset facilitating multimodal and
Synergetic sociAL Scene Analysis, and make two main contributions to research
on automated social interaction analysis: (1) SALSA records social interactions
among 18 participants in a natural, indoor environment for over 60 minutes,
under the poster presentation and cocktail party contexts presenting
difficulties in the form of low-resolution images, lighting variations,
numerous occlusions, reverberations and interfering sound sources; (2) To
alleviate these problems we facilitate multimodal analysis by recording the
social interplay using four static surveillance cameras and sociometric badges
worn by each participant, comprising the microphone, accelerometer, bluetooth
and infrared sensors. In addition to raw data, we also provide annotations
concerning individuals' personality as well as their position, head, body
orientation and F-formation information over the entire event duration. Through
extensive experiments with state-of-the-art approaches, we show (a) the
limitations of current methods and (b) how the recorded multiple cues
synergetically aid automatic analysis of social interactions. SALSA is
available at http://tev.fbk.eu/salsa.Comment: 14 pages, 11 figure
Information theory tools for viewpoint selection, mesh saliency and geometry simplification
In this chapter we review the use of an information channel as a unified framework for viewpoint selection, mesh saliency and geometry simplification. Taking the viewpoint distribution as input and object mesh polygons as output vectors, the channel is given by the projected areas of the polygons over the different viewpoints. From this channel, viewpoint entropy and viewpoint mutual information can be defined in a natural way. Reversing this channel, polygonal mutual information is obtained, which is interpreted as an ambient occlusion-like quantity, and from the variation of this polygonal mutual information mesh saliency is defined. Viewpoint entropy, viewpoint Kullback-Leibler distance, and viewpoint mutual information are then applied to mesh simplification, and shown to compare well with a classical geometrical simplification method
RMSC: Robust Modeling of Subspace Clustering for high dimensional data
Subspace clustering is one of the active research problem associated with high-dimensional data. Here some of the standard techniques are reviewed to investigate existing methodologies. Although, there have been various forms of research techniques evolved recently, they do not completely mitigate the problems pertaining to noise sustainability and optimization of clustering accuracy. Hence, a novel technique called as Robust Modeling of Subspace Clustering (RMSC) presented to solve the above problem. An analytical research methodology is used to formulate two algorithms for computing outliers and for extracting elite subspace from the highdimensional data inflicted by different forms of noise. RMSC was found to offer higher accuracy and lower error rate both in presence of noise and absence of noise over high-dimensional data. © 2017 IEEE
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