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66
Object Tracking: A Survey
, 2006
"... The goal of this article is to review the stateoftheart tracking methods, classify them into different categories, and identify new trends. Object tracking, in general, is a challenging problem. Difficulties in tracking objects can arise due to abrupt object motion, changing appearance patterns o ..."
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Cited by 701 (7 self)
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The goal of this article is to review the stateoftheart tracking methods, classify them into different categories, and identify new trends. Object tracking, in general, is a challenging problem. Difficulties in tracking objects can arise due to abrupt object motion, changing appearance patterns of both the object and the scene, nonrigid object structures, objecttoobject and objecttoscene occlusions, and camera motion. Tracking is usually performed in the context of higherlevel applications that require the location and/or shape of the object in every frame. Typically, assumptions are made to constrain the tracking problem in the context of a particular application. In this survey, we categorize the tracking methods on the basis of the object and motion representations used, provide detailed descriptions of representative methods in each category, and examine their pros and cons. Moreover, we discuss the important issues related to tracking including the use of appropriate image features, selection of motion models, and detection of objects.
Probabilistic Graph and Hypergraph Matching
"... We consider the problem of finding a matching between two sets of features, given complex relations among them, going beyond pairwise. Each feature set is modeled by a hypergraph where the complex relations are represented by hyperedges. A match between the feature sets is then modeled as a hypergr ..."
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Cited by 67 (0 self)
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We consider the problem of finding a matching between two sets of features, given complex relations among them, going beyond pairwise. Each feature set is modeled by a hypergraph where the complex relations are represented by hyperedges. A match between the feature sets is then modeled as a hypergraph matching problem. We derive the hypergraph matching problem in a probabilistic setting represented by a convex optimization. First, we formalize a soft matching criterion that emerges from a probabilistic interpretation of the problem input and output, as opposed to previous methods that treat soft matching as a mere relaxation of the hard matching problem. Second, the model induces an algebraic relation between the hyperedge weight matrix and the desired vertextovertex probabilistic matching. Third, the model explains some of the graph matching normalization proposed in the past on a heuristic basis such as doubly stochastic normalizations of the edge weights. A key benefit of the model is that the global optimum of the matching criteria can be found via an iterative successive projection algorithm. The algorithm reduces to the well known Sinkhorn [15] row/column matrix normalization procedure in the special case when the two graphs have the same number of vertices and a complete matching is desired. Another benefit of our model is the straightforward scalability from graphs to hypergraphs.
Continuous Capture of SkinDeformation
"... We describe a method for the acquisition of deformable human geometry from silhouettes. Our technique uses a commercial tracking system to determine the motion of the skeleton, then estimates geometry for each bone using constraints provided by the silhouettes from one or more cameras. These silhou ..."
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Cited by 61 (2 self)
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We describe a method for the acquisition of deformable human geometry from silhouettes. Our technique uses a commercial tracking system to determine the motion of the skeleton, then estimates geometry for each bone using constraints provided by the silhouettes from one or more cameras. These silhouettes do not give a complete characterization of the geometry for a particular point in time, but when the subject moves, many observations of the same local geometries allow the construction of a complete model. Our reconstruction algorithm provides a simple mechanism for solving the problems of view aggregation, occlusion handling, hole filling, noise removal, and deformation modeling. The resulting model is parameterized to synthesize geometry for new poses of the skeleton. We demonstrate this capability by rendering the geometry for motion sequences that were not included in the original datasets.
Passive Photometric Stereo from Motion
"... We introduce an iterative algorithm for shape reconstruction from multiple images of a moving (Lambertian) object illuminated by distant (and possibly time varying) lighting. Starting with an initial piecewise linear surface, the algorithm iteratively estimates a new surface based on the previous su ..."
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Cited by 45 (2 self)
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We introduce an iterative algorithm for shape reconstruction from multiple images of a moving (Lambertian) object illuminated by distant (and possibly time varying) lighting. Starting with an initial piecewise linear surface, the algorithm iteratively estimates a new surface based on the previous surface estimate and the photometric information available from the input image sequence. During each iteration, standard photometric stereo techniques are applied to estimate the surface normals up to an unknown generalized basrelief transform, and a new surface is computed by integrating the estimated normals. The algorithm essentially consists of a sequence of matrix factorizations (of intensity values) followed by minimization using gradient descent (integration of the normals). Conceptually, the algorithm admits a clear geometric interpretation, which is used to provide a qualitative analysis of the algorithm’s convergence. Implementationwise, it is straightforward, being based on several established photometric stereo and structure from motion algorithms. We demonstrate experimentally the effectiveness of our algorithm using several videos of handheld objects moving in front of a fixed light and camera.
Automatic NonRigid 3D Modeling from Video
 IN ECCV
, 2004
"... We present a robust framework for estimating nonrigid 3D shape and motion in video sequences. Given an input video sequence, and a userspecified region to reconstruct, the algorithm automatically solves for the 3D timevarying shape and motion of the object, and estimates which pixels are outl ..."
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Cited by 32 (3 self)
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We present a robust framework for estimating nonrigid 3D shape and motion in video sequences. Given an input video sequence, and a userspecified region to reconstruct, the algorithm automatically solves for the 3D timevarying shape and motion of the object, and estimates which pixels are outliers, while learning all system parameters, including a PDF over nonrigid deformations. There are no usertuned parameters (other than initialization); all parameters are learned by maximizing the likelihood of the entire image stream. We apply our method to both rigid and nonrigid shape reconstruction, and demonstrate it in challenging cases of occlusion and variable illumination.
IlluminationInvariant Tracking via Graph Cuts
 IN PROC. OF IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION
, 2005
"... Illumination changes are a ubiquitous problem in computer vision. They present a challenge in many applications, including tracking: for example, an object may move in and out of a shadow. We present a new tracking algorithm which is insensitive to illumination changes, while at the same time using ..."
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Cited by 30 (0 self)
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Illumination changes are a ubiquitous problem in computer vision. They present a challenge in many applications, including tracking: for example, an object may move in and out of a shadow. We present a new tracking algorithm which is insensitive to illumination changes, while at the same time using all of the available photometric information. The algorithm is based on computing an illuminationinvariant optical flow field; the computation is made robust by using a graph cuts formulation. Experimentally, the new technique is shown to quite reliable in both synthetic and real sequences, dealing with a variety of illumination changes that cause problems for density based trackers.
Conditional Filters for Image Sequence Based Tracking  Application to Point Tracking
 IEEE TRANSACTIONS ON IMAGE PROCESSING
, 2005
"... In this paper, a new conditional formulation of classical filtering methods is proposed. This formulation is dedicated to image sequence based tracking. These conditional filters allow solving systems whose measurements and state equation are estimated from the image data. In particular, the model ..."
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Cited by 25 (9 self)
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In this paper, a new conditional formulation of classical filtering methods is proposed. This formulation is dedicated to image sequence based tracking. These conditional filters allow solving systems whose measurements and state equation are estimated from the image data. In particular, the model that is considered for point tracking combines a state equation relying on the optical flow constraint and measurements provided by a matching technique. Based on this, two point trackers are derived. The first one is a linear tracker wellsuited to image sequences exhibiting global dominant motion. This filter is determined through the use of a new estimator, called the conditional linear minimum variance estimator. The second one is a nonlinear tracker, implemented from a conditional particle filter. It allows tracking of points whose motion may be only locally described. These conditional trackers significantly improve results in some general situations. In particular, they allow dealing with noisy sequences, abrupt changes of trajectories, occlusions and cluttered background.
"Shape Activity": A Continuous State HMM for Moving/Deforming Shapes with Application to Abnormal Activity Detection
"... The aim is to model "activity" performed by a group of moving and interacting objects (which can be people or cars or different rigid components of the human body) and use the models for abnormal activity detection. Previous approaches to modeling group activity include cooccurrence stati ..."
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Cited by 25 (11 self)
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The aim is to model "activity" performed by a group of moving and interacting objects (which can be people or cars or different rigid components of the human body) and use the models for abnormal activity detection. Previous approaches to modeling group activity include cooccurrence statistics (individual and joint histograms) and Dynamic Bayesian Networks, neither of which is applicable when the number of interacting objects is large. We treat the objects as point objects (referred to as "landmarks") and propose to model their changing configuration as a moving and deforming "shape" (using Kendall's shape theory for discrete landmarks). A continuous state Hidden Markov Model (HMM) is defined for landmark shape dynamics in an activity. The configuration of landmarks at a given time forms the observation vector and the corresponding shape and the scaled Euclidean motion parameters form the hidden state vector. An abnormal activity is then defined as a change in the shape activity model, which could be slow or drastic and whose parameters are unknown. Results are shown on a real abnormal activity detection problem involving multiple moving objects.
Perspective nonrigid shape and motion recovery
 In Proc. European Conference on Computer Vision
, 2008
"... Abstract. We present a closed form solution to the nonrigid shape and motion (NRSM) problem from point correspondences in multiple perspective uncalibrated views. Under the assumption that the nonrigid object deforms as a linear combination of K rigid shapes, we show that the NRSM problem can be vie ..."
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Cited by 24 (1 self)
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Abstract. We present a closed form solution to the nonrigid shape and motion (NRSM) problem from point correspondences in multiple perspective uncalibrated views. Under the assumption that the nonrigid object deforms as a linear combination of K rigid shapes, we show that the NRSM problem can be viewed as a reconstruction problem from multiple projections from P 3K to P 2. Therefore, one can linearly solve for the projection matrices by factorizing a multifocal tensor. However, this projective reconstruction in P 3K does not satisfy the constraints of the NRSM problem, because it is computed only up to a projective transformation in P 3K. Our key contribution is to show that, by exploiting algebraic dependencies among the entries of the projection matrices, one can upgrade the projective reconstruction to determine the affine configuration of the points in R 3, and the motion of the camera relative to their centroid. Moreover, if K ≥ 2, then either by using calibrated cameras, or by assuming a camera with fixed internal parameters, it is possible to compute the Euclidean structure by a closed form method. 1
Partial Linear Gaussian Models for Tracking in Image Sequences Using Sequential Monte Carlo Methods
 JOURNAL OF COMPUTER VISION
, 2007
"... The recent development of Sequential Monte Carlo methods (also called particle filters) has enabled the definition of efficient algorithms for tracking applications in image sequences. The efficiency of these approaches depends on the quality of the statespace exploration, which may be inefficient ..."
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Cited by 22 (3 self)
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The recent development of Sequential Monte Carlo methods (also called particle filters) has enabled the definition of efficient algorithms for tracking applications in image sequences. The efficiency of these approaches depends on the quality of the statespace exploration, which may be inefficient due to a crude choice of the function used to sample in the associated probability space. A careful study of this issue led us to consider the modeling of the tracked dynamic system with partial linear Gaussian models. Such models are characterized by a non linear dynamic equation, a linear measurement equation and additive Gaussian noises. They allow inferring an analytic expression of the optimal importance function used in the diffusion process of the particle filter, and enable building a relevant approximation of a validation gate. Despite of these potential advantages partial linear Gaussian models have not been investigated. The aim of this paper is therefore to demonstrate that such models can be of real interest facing difficult usual issues such as occlusions, ambiguities due to cluttered backgrounds and large state space. Three instances of these models are proposed. After a theoretical analysis, their significance is demonstrated by their performance for tracking points and planar objects in challenging realworld image sequences.