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Object Detection and Matching with Mobile Cameras Collaborating with Fixed Cameras

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Robust, High-Speed Interest Point Matching for Real-Time Applications

Michael Calonder

A typical Computer Vision system needs to process vast amounts of data as captured by one or more cameras, constantly testing the capabilities of today's hardware. Yet such systems face an ever-growing computational load caused by the more and more demandi ...

EPFL2010

We propose a method to compute scale invariant features in omnidirectional images. We present a formulation based on Riemannian geometry for the definition of differential operators on non-Euclidian manifolds that describe the mirror and lens structure in ...

2010

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While feature point recognition is a key component of modern approaches to object detection, existing approaches require computationally expensive patch preprocessing to handle perspective distortion. In this paper, we show that formulating the problem in ...

2010

Sampling-aware Polar Descriptors on the Sphere

Pascal Frossard, Zafer Arican

We present a new descriptor and feature matching solution for omnidirectional images. The descriptor builds on the log-polar planar descriptors, but adapts to the specific geometry and non-uniform sampling density of spherical images. We further propose a ...

2010

A Master-Slave Approach to Detect and Match Objects Across Several Uncalibrated Moving Cameras

Michel Bierlaire, Pierre Vandergheynst, Murat Kunt, Alexandre Massoud Alahi

Most multi-camera systems assume a well structured environment to detect and match objects across cameras. Cameras need to be fixed and calibrated. In this work, a novel system is presented to detect and match any objects in a network of uncalibrated fixed ...

2009

Face tracking in video sequences based on multiple local features and high-light free color information

Sofia Karygianni

This thesis presents an algorithm for face tracking in video sequences. We investigate the application of affine invariant, local features for face tracking under random poses and expressions. In order to capture as much as possible of the facial variabili ...

2009

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We present an algorithm for clustering sets of detected interest points into groups that correspond to visually distinct structure. Through the use of a suitable colour and texture representation, our clustering method is able to identify keypoints that be ...

2009

Fast Human Detection in Videos using Joint Appearance and Foreground Learning from Covariances of Image Feature Subsets

Jean-Marc Odobez

We present a fast method to detect humans from stationary surveillance videos. Traditional approaches exploit background subtraction as an attentive filter, by applying the still image detectors only on foreground regions. This doesn't take into account th ...

Idiap2009

Object Detection and Matching in a Mixed Network of Fixed and Mobile Cameras

Michel Bierlaire, Pierre Vandergheynst, Murat Kunt, Alexandre Massoud Alahi

This work tackles the challenge of detecting and matching objects in scenes observed simultaneously by fixed and mobile cameras. No calibration between the cameras is needed, and no training data is used. A fully automated system is presented to detect if ...

2008

A Master-Slave Approach for Object Detection and Matching with Fixed and Mobile Cameras

Michel Bierlaire, Murat Kunt, Alexandre Massoud Alahi

Typical object detection algorithms on mobile cameras suffer from the lack of a-priori knowledge on the object to be detected. The variability in the shape, pose, color distribution, and behavior affect the robustness of the detection process. In general, ...

2008