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Privacy preserving crowd monitoring: Counting people without people models or tracking
We present a privacy-preserving system for estimating the size of inhomogeneous crowds, composed of pedestrians that travel in different directions, without using explicit object segmentation orExpand
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Supervised Learning of Semantic Classes for Image Annotation and Retrieval
A probabilistic formulation for semantic image annotation and retrieval is proposed. Annotation and retrieval are posed as classification problems where each class is defined as the group of databaseExpand
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Modeling, Clustering, and Segmenting Video with Mixtures of Dynamic Textures
A dynamic texture is a spatio-temporal generative model for video, which represents video sequences as observations from a linear dynamical system. This work studies the mixture of dynamic textures,Expand
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Counting People With Low-Level Features and Bayesian Regression
An approach to the problem of estimating the size of inhomogeneous crowds, which are composed of pedestrians that travel in different directions, without using explicit object segmentation orExpand
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3D Human Pose Estimation from Monocular Images with Deep Convolutional Neural Network
In this paper, we propose a deep convolutional neural network for 3D human pose estimation from monocular images. We train the network using two strategies: (1) a multi-task framework that jointlyExpand
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Classifying Video with Kernel Dynamic Textures
The dynamic texture is a stochastic video model that treats the video as a sample from a linear dynamical system. The simple model has been shown to be surprisingly useful in domains such as videoExpand
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Probabilistic kernels for the classification of auto-regressive visual processes
We present a framework for the classification of visual processes that are best modeled with spatio-temporal autoregressive models. The new framework combines the modeling power of a family of modelsExpand
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Learning Dynamic Memory Networks for Object Tracking
Template-matching methods for visual tracking have gained popularity recently due to their comparable performance and fast speed. However, they lack effective ways to adapt to changes in the targetExpand
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Analysis of Crowded Scenes using Holistic Properties
We present results on the PETS 2009 dataset using surveillance systems based on holistic properties of the video. In particular, we evaluate a crowd counting system, based on regression of holisticExpand
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Maximum-Margin Structured Learning with Deep Networks for 3D Human Pose Estimation
This paper focuses on structured-output learning using deep neural networks for 3D human pose estimation from monocular images. Our network takes an image and 3D pose as inputs and outputs a scoreExpand
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