combining generative and discriminative models in a framework for articulated pose estimation we develop a method for the estimation of articulated pose such as that of the human body or the human hand from a single monocular image pose estimation is formulated as a statistical inference problem where the goal is to find a posterior probability distribution over poses as well as a maximum a posteriori map estimate the method combines two modeling approaches one discriminative and the other generative the discriminative model consists of a set of mapping functions that are constructed automatically from a labeled training set of body poses and their respective image features the discriminative formulation allows for modeling ambiguous one to many mappings through the use of multi modal distributions that may yield multiple valid articulated pose hypotheses from a single image the generative model is defined in terms of a computer graphics rendering of poses while the generative model offers an accurate way to relate observed image features and hidden body pose random variables it is difficult to use it directly in pose estimation since inference is computationally intractable in contrast inference with the discriminative model is tractable but considerably less accurate for the problem of interest a combined discriminative generative formulation is derived that leverages the complimentary strengths of both models in a principled framework for articulated pose inference two efficient map pose estimation algorithms are derived from this formulation the first is deterministic and the second non deterministic performance of the framework is quantitatively evaluated in estimating articulated pose of both the human hand and human body

a consensus sampling technique for fast and robust model fitting in this paper a new algorithm is proposed to improve the efficiency and robustness of random sampling consensus ransac without prior information about the error scale three techniques are developed in an iterative hypothesis and evaluation framework firstly we propose a consensus sampling technique to increase the probability of sampling inliers by exploiting the feedback information obtained from the evaluation procedure secondly the preemptive multiple k th order approximation pmka is developed for efficient model evaluation with unknown error scale furthermore we propose a coarse to fine strategy for the robust standard deviation estimation to determine the unknown error scale experimental results of the fundamental matrix computation on both simulated and real data are shown to demonstrate the superiority of the proposed algorithm over the previous methods

a compact association of particle filtering and kernel based object tracking particle filtering pf and kernel based object tracking kbot algorithms have shown their promises in a wide range of visual tracking contexts this paper mainly addresses the association of pf and kbot unlike other related association approaches which usually directly use kbot to refine the position states of propagated particles for more accurate mode seeking we elucidate the problem of what kind of particles is suitable for employing kbot to refine their position states from a theoretical point of view in accordance with the theoretical analysis a two stage solution is also proposed to resample propagated particles that are suitable for invoking kbot from a computational perspective the incremental bhattacharyya dissimilarity ibd based stage is designed to consistently distinguish the particles located in the object region from the others placed in the background while the matrix condition number based stage is formulated to further eliminate the particles positioned at the ill posed conditions for running kbot once the appropriate particles are obtained constrained gradient based mean shift optimization enables us to efficiently refine the particles position states besides a state transition model embodying object scale oriented information and prior motion cues is presented to adapt to fast movement scenarios these ingredients lead to a new tracking algorithm experiments demonstrate that the proposed association approach is more robust to handle complex tracking conditions in comparison with related methods also a limited number of particles are used in our association algorithm to maintain multiple hypotheses

recognizing multiple human activities and tracking full body pose in unconstrained environments visual observations such as camera images are hard to obtain for long term human motion analysis in unconstrained environments in this paper we present a method for human full body pose tracking and activity recognition from measurements of few body worn inertial orientation sensors the sensors make our approach insensitive to illumination and occlusions and permit a person to move freely since the data provided by inertial sensors is sparse noisy and often ambiguous we use a generative prior model of feasible human poses and movements to constrain the tracking problem our model consists of several low dimensional activity specific manifold embeddings that significantly restrict the search space for pose tracking using a particle filter our method continuously explores multiple pose hypotheses in the embedding space an efficient activity switching mechanism governs the distribution of particles across the activity specific manifold embeddings selecting a pose hypothesis that best explains incoming sensor observations simultaneously allows us to classify the activity a person is performing and to estimate the full body pose we also derive an effective measure of predictive confidence that enables detecting anomalous movements experiments on a multi person data set containing several activities show that our method can seamlessly detect activity switches and accurately reconstruct full body poses from the data of only six wearable inertial sensors

robust clock synchronization in wireless sensor networks through noise density estimation assuming that the network delays are normally distributed and the network nodes are subject to clock phase offset errors the maximum likelihood estimator mle and the kalman filter kf have been recently proposed with the goal of maximizing the clock synchronization accuracy in wireless sensor networks wsns however because the network delays may assume any distribution and the performance of mle and kf is quite sensitive to the distribution of network delays designing clock synchronization algorithms that are robust to arbitrary network delay distributions appears as an important problem adopting a bayesian framework this paper proposes a novel clock synchronization algorithm called the iterative gaussian mixture kalman particle filter igmkpf which combines the gaussian mixture kalman particle filter gmkpf with an iterative noise density estimation procedure to achieve robust performance in the presence of unknown network delay distributions the kullback leibler divergence is used as a measure to assess the departure of estimated observation noise density from its true expression the posterior cramer rao bound pcrb and the mean square error mse of igmkpf are evaluated via computer simulations it is shown that igmkpf exhibits improved performance and robustness relative to mle the prior information plays an important role in igmkpf a mle based method for obtaining reliable prior information for clock phase offsets is presented and shown to ensure the convergence of igmkpf

fitting parameterized three dimensional models to images model based recognition and motion tracking depend upon the ability to solve for projection and model parameters that will best fit a 3 d model to matching 2 d image features the author extends current methods of parameter solving to handle objects with arbitrary curved surfaces and with any number of internal parameters representing articulation variable dimensions or surface deformations numerical stabilization methods are developed that take account of inherent inaccuracies in the image measurements and allow useful solutions to be determined even when there are fewer matches than unknown parameters the levenberg marquardt method is used to always ensure convergence of the solution these techniques allow model based vision to be used for a much wider class of problems than was possible with previous methods their application is demonstrated for tracking the motion of curved parameterized objects

semi implicit finite volume scheme for image processing in 3d cylindrical geometry nowadays 3d echocardiography is a well known technique in medical diagnosis inexpensive echocardiographic acquisition devices are applied to scan 2d slices rotated along a prescribed direction then the discrete 3d image information is given on a cylindrical grid usually this original discrete image intensity function is interpolated to a uniform rectangular grid and then numerical schemes for 3d image processing operations e g nonlinear smoothing in the uniform rectangular geometry are used however due to the generally large amount of noise present in echocardiographic images the interpolation step can yield undesirable results in this paper we avoid this step and suggest a 3d finite volume method for image selective smoothing directly in the cylindrical image geometry specifically we study a semi implicit 3d cylindrical finite volume scheme for solving a perona malik type nonlinear diffusion equation and apply the scheme to 3d cylindrical echocardiographic images the l stability and convergence of the scheme to the weak solution of the regularized perona malik equation is proved

scale space and edge detection using anisotropic diffusion a new definition of scale space is suggested and a class of algorithms used to realize a diffusion process is introduced the diffusion coefficient is chosen to vary spatially in such a way as to encourage intraregion smoothing rather than interregion smoothing it is shown that the no new maxima should be generated at coarse scales property of conventional scale space is preserved as the region boundaries in the approach remain sharp a high quality edge detector which successfully exploits global information is obtained experimental results are shown on a number of images parallel hardware implementations are made feasible because the algorithm involves elementary local operations replicated over the image

monocular human motion tracking by using de mc particle filter tracking human motion from monocular video sequences has attracted significantly increased interests in recent years a key to accomplishing this task is to efficiently explore a high dimensional state space however the traditional particle filter method and many of its variants have not been able to meet expectations as they lack a strategy to do efficiently sampling or stochastic search we present a novel approach namely differential evolution markov chain de mc particle filtering by taking the advantage of the de mc algorithm s ability to approximate complicated distributions substantial improvement can be made to the traditional structure of the particle filter as a result an efficient stochastic search can be performed to locate the modes of likelihoods furthermore we apply the proposed algorithm to solve the 3d articulated model based human motion tracking problem a reliable image likelihood function is built for visual tracker design based on the proposed de mc particle filter and the image likelihood function we perform a variety of monocular human motion tracking experiments experimental results including the comparison with the performance of other particle filtering methods demonstrate the reliable tracking performance of the proposed approach

a study on smoothing for particle filtered 3d human body tracking stochastic models have become the dominant means of approaching the problem of articulated 3d human body tracking where approximate inference is employed to tractably estimate the high dimensional 30d posture space of these approximate inference techniques particle filtering is the most commonly used approach however filtering only takes into account past observations almost no body tracking research employs smoothing to improve the filtered inference estimate despite the fact that smoothing considers both past and future evidence and so should be more accurate in an effort to objectively determine the worth of existing smoothing algorithms when applied to human body tracking this paper investigates three approximate smoothed inference techniques particle filtered backwards smoothing variational approximation and gibbs sampling results are quantitatively evaluated on both the humaneva dataset as well as a scene containing occluding clutter surprisingly it is found that existing smoothing techniques are unable to provide much improvement on the filtered estimate and possible reasons as to why are explored and discussed

dynamic 3d models with local and global deformations deformable superquadrics the authors present a physically based approach to fitting complex three dimensional shapes using a novel class of dynamic models that can deform both locally and globally they formulate the deformable superquadrics which incorporate the global shape parameters of a conventional superellipsoid with the local degrees of freedom of a spline the model s six global deformational degrees of freedom capture gross shape features from visual data and provide salient part descriptors for efficient indexing into a database of stored models the local deformation parameters reconstruct the details of complex shapes that the global abstraction misses the equations of motion which govern the behavior of deformable superquadrics make them responsive to externally applied forces the authors fit models to visual data by transforming the data into forces and simulating the equations of motion through time to adjust the translational rotational and deformational degrees of freedom of the models model fitting experiments involving 2d monocular image data and 3d range data are presented

a lattice based mrf model for dynamic near regular texture tracking a near regular texture nrt is a geometric and photometric deformation from its regular origin a congruent wallpaper pattern formed by 2d translations of a single tile a dynamic nrt is an nrt under motion although nrts are pervasive in man made and natural environments effective computational algorithms for nrts are few this paper addresses specific computational challenges in modeling and tracking dynamic nrts including ambiguous correspondences occlusions and drastic illumination and appearance variations we propose a lattice based markov random field mrf model for dynamic nrts in a 3d spatiotemporal space our model consists of a global lattice structure that characterizes the topological constraint among multiple textons and an image observation model that handles local geometry and appearance variations based on the proposed mrf model we develop a tracking algorithm that utilizes belief propagation and particle filtering to effectively handle the special challenges of the dynamic nrt tracking without any assumption on the motion types or lighting conditions we provide quantitative evaluations of the proposed method against existing tracking algorithms and demonstrate its applications in video editing

particle filters for state space models with the presence of unknown static parameters particle filters for dynamic state space models handling unknown static parameters are discussed the approach is based on marginalizing the static parameters out of the posterior distribution such that only the state vector needs to be considered such a marginalization can always be applied however real time applications are only possible when the distribution of the unknown parameters given both observations and the hidden state vector depends on some low dimensional sufficient statistics such sufficient statistics are present in many of the commonly used state space models marginalizing the static parameters avoids the problem of impoverishment which typically occurs when static parameters are included as part of the state vector the filters are tested on several different models with promising results

variational maximum a posteriori by annealed mean field analysis this paper proposes a novel probabilistic variational method with deterministic annealing for the maximum a posteriori map estimation of complex stochastic systems since the map estimation involves global optimization in general it is very difficult to achieve therefore most probabilistic inference algorithms are only able to achieve either the exact or the approximate posterior distributions our method constrains the mean field variational distribution to be multivariate gaussian then a deterministic annealing scheme is nicely incorporated into the mean field fix point iterations to obtain the optimal map estimate this is based on the observation that when the covariance of the variational gaussian distribution approaches to zero the infimum point of the kullback leibler kl divergence between the variational gaussian and the real posterior would be the same as the supreme point of the real posterior although global optimality may not be guaranteed our extensive synthetic and real experiments demonstrate the effectiveness and efficiency of the proposed method

affine kac moody symmetric spaces related with a1 1 a2 1 a2 2 symmetric spaces associated with lie algebras and lie groups which are riemannian manifolds have recently got a lot of attention in various branches of physics for their role in classical quantum integrable systems transport phenomena etc their infinite dimensional counter parts have recently been discovered which are affine kac moody symmetric spaces in this paper we have algebraically explicitly computed the affine kac moody symmetric spaces associated with affine kac moody algebras a1 1 a2 1 a2 2 we hope these types of spaces will play similar roles as that of symmetric spaces in many physical systems

efficient particle filtering via sparse kernel density estimation particle filters pfs are bayesian filters capable of modeling nonlinear non gaussian and nonstationary dynamical systems recent research in pfs has investigated ways to appropriately sample from the posterior distribution maintain multiple hypotheses and alleviate computational costs while preserving tracking accuracy to address these issues a novel utilization of the support vector data description svdd density estimation method within the particle filtering framework is presented the svdd density estimate can be integrated into a wide range of pfs to realize several benefits it yields a sparse representation of the posterior density that reduces the computational complexity of the pf the proposed approach also provides an analytical expression for the posterior distribution that can be used to identify its modes for maintaining multiple hypotheses and computing the map estimate and to directly sample from the posterior we present several experiments that demonstrate the advantages of incorporating a sparse kernel density estimate in a particle filter

coupled prediction classification for robust visual tracking this paper addresses the problem of robust template tracking in image sequences our work falls within the discriminative framework in which the observations at each frame yield direct probabilistic predictions of the state of the target our primary contribution is that we explicitly address the problem that the prediction accuracy for different observations varies and in some cases can be very low to this end we couple the predictor to a probabilistic classifier which when trained can determine the probability that a new observation can accurately predict the state of the target that is determine the relevance or reliability of the observation in question in the particle filtering framework we derive a recursive scheme for maintaining an approximation of the posterior probability of the state in which multiple observations can be used and their predictions moderated by their corresponding relevance in this way the predictions of the relevant observations are emphasized while the predictions of the irrelevant observations are suppressed we apply the algorithm to the problem of 2d template tracking and demonstrate that the proposed scheme outperforms classical methods for discriminative tracking both in the case of motions which are large in magnitude and also for partial occlusions

efficient visual tracking using particle filter with incremental likelihood calculation in this paper we propose a particle filter that determines the weight of each particle employing the incremental likelihood calculation since there is usually a large overlap region between the two particles that are sequentially generated determining the weight of the particle has only a small time cost therefore the real time performance of the proposed tracker can be dramatically improved extensive experimental results for single object and multiple object tracking scenarios are presented to demonstrate the efficiency of the proposed approach finally an interesting color based active vision system is developed for a free floating space robot testbed to facilitate teleoperation

active contours for tracking distributions a new approach to tracking using active contours is presented the class of objects to be tracked is assumed to be characterized by a probability distribution over some variable such as intensity color or texture the goal of the algorithm is to find the region within the current image such that the sample distribution of the interior of the region most closely matches the model distribution two separate criteria for matching distributions are examined and the curve evolution equations are derived in each case the flows are shown to perform well in experiments

a generic framework for tracking using particle filter with dynamic shape prior tracking deforming objects involves estimating the global motion of the object and its local deformations as functions of time tracking algorithms using kalman filters or particle filters pfs have been proposed for tracking such objects but these have limitations due to the lack of dynamic shape information in this paper we propose a novel method based on employing a locally linear embedding in order to incorporate dynamic shape information into the particle filtering framework for tracking highly deformable objects in the presence of noise and clutter the pf also models image statistics such as mean and variance of the given data which can be useful in obtaining proper separation of object and background

c ondensation conditional density propagation forvisual tracking the problem of tracking curves in dense visual clutter is challenging kalman filtering is inadequate because it is based on gaussian densities which being unimo dal cannot represent simultaneous alternative hypotheses the condensation algorithm uses factored sampling previously applied to the interpretation of static images in which the probability distribution of possible interpretations is represented by a randomly generated set condensation uses learned dynamical models together with visual observations to propagate the random set over time the result is highly robust tracking of agile motion notwithstanding the use of stochastic methods the algorithm runs in near real time

statistical principles in image modeling images of natural scenes contain a rich variety of visual patterns to learn and recognize these patterns from natural images it is necessary to construct statistical models for these patterns in this review article we describe three statistical principles for modeling image patterns the sparse coding principle the minimax entropy principle and the meaningful alignment principle we explain these three principles and their relationships in the context of modeling images as compositions of gabor wavelets these three principles correspond to three regimes of composition patterns of gabor wavelets and these three regimes are connected by changes in scale or resolution

snakes active contour models a snake is an energy minimizing spline guided by external constraint forces and influenced by image forces that pull it toward features such as lines and edges snakes are active contour models they lock onto nearby edges localizing them accurately scale space continuation can be used to enlarge the capture region surrounding a feature snakes provide a unified account of a number of visual problems including detection of edges lines and subjective contours motion tracking and stereo matching we have used snakes successfully for interactive interpretation in which user imposed constraint forces guide the snake near features of interest

linearized fe approximations to a nonlinear gradient flow we study fully discrete linearized galerkin finite element approximations to a nonlinear gradient flow applications of which can be found in many areas due to the strong nonlinearity of the equation existing analyses for implicit schemes require certain restrictions on the time step and no analysis has been explored for linearized schemes this paper focuses on the unconditionally optimal l 2 error estimate of a linearized scheme the key to our analysis is an iterated sequence of time discrete elliptic equations and a rigorous analysis of its solution we prove the w 1 infty boundedness of the solution of the time discrete system and the corresponding finite element solution based on a more precise estimate of elliptic pdes in w 2 2 epsilon 1 and h 2 epsilon 2 and a physical feature of the gradient dependent diffusion coefficient numerical examples are provided to support our theoretical analysis

a gaussian process guided particle filter for tracking 3d human pose in video in this paper we propose a hybrid method that combines gaussian process learning a particle filter and annealing to track the 3d pose of a human subject in video sequences our approach which we refer to as annealed gaussian process guided particle filter comprises two steps in the training step we use a supervised learning method to train a gaussian process regressor that takes the silhouette descriptor as an input and produces multiple output poses modeled by a mixture of gaussian distributions in the tracking step the output pose distributions from the gaussian process regression are combined with the annealed particle filter to track the 3d pose in each frame of the video sequence our experiments show that the proposed method does not require initialization and does not lose tracking of the pose we compare our approach with a standard annealed particle filter using the humaneva i dataset and with other state of the art approaches using the humaneva ii dataset the evaluation results show that our approach can successfully track the 3d human pose over long video sequences and give more accurate pose tracking results than the annealed particle filter

nonlinear multiscale analysis of three dimensional echocardiographic sequences introduces a new model for multiscale analysis of space time echocardiographic sequences the proposed nonlinear partial differential equation representing the multiscale analysis filters the sequence while keeping the space time coherent structures it combines the ideas of regularized perona malik anisotropic diffusion and the galilean invariant movie multiscale analysis of alvarez et al arch rat mech anal vol 123 p 200 57 1993 a numerical method for solving the proposed partial differential equation is suggested and its stability is shown computational results on synthesized and real sequences are provided a qualitative and quantitative evaluation of the accuracy of the method is presented

a stochastic filtering technique for fluid flow velocity fields tracking in this paper we present a method for the temporal tracking of fluid flow velocity fields the technique we propose is formalized within a sequential bayesian filtering framework the filtering model combines an ito diffusion process coming from a stochastic formulation of the vorticity velocity form of the navier stokes equation and discrete measurements extracted from the image sequence in order to handle a state space of reasonable dimension the motion field is represented as a combination of adapted basis functions derived from a discretization of the vorticity map of the fluid flow velocity field the resulting nonlinear filtering problem is solved with the particle filter algorithm in continuous time an adaptive dimensional reduction method is applied to the filtering technique relying on dynamical systems theory the efficiency of the tracking method is demonstrated on synthetic and real world sequences

learning generative models for multi activity body pose estimation we present a method to simultaneously estimate 3d body pose and action categories from monocular video sequences our approach learns a generative model of the relationship of body pose and image appearance using a sparse kernel regressor body poses are modelled on a low dimensional manifold obtained by locally linear embedding dimensionality reduction in addition we learn a prior model of likely body poses and a dynamical model in this pose manifold sparse kernel regressors capture the nonlinearities of this mapping efficiently within a recursive bayesian sampling framework the potentially multimodal posterior probability distributions can then be inferred an activity switching mechanism based on learned transfer functions allows for inference of the performed activity class along with the estimation of body pose and 2d image location of the subject using a rough foreground segmentation we compare binary pca and distance transforms to encode the appearance as a postprocessing step the globally optimal trajectory through the entire sequence is estimated yielding a single pose estimate per frame that is consistent throughout the sequence we evaluate the algorithm on challenging sequences with subjects that are alternating between running and walking movements our experiments show how the dynamical model helps to track through poorly segmented low resolution image sequences where tracking otherwise fails while at the same time reliably classifying the activity type

multi target tracking by online learning a crf model of appearance and motion patterns we introduce an online learning approach for multi target tracking detection responses are gradually associated into tracklets in multiple levels to produce final tracks unlike most previous approaches which only focus on producing discriminative motion and appearance models for all targets we further consider discriminative features for distinguishing difficult pairs of targets the tracking problem is formulated using an online learned crf model and is transformed into an energy minimization problem the energy functions include a set of unary functions that are based on motion and appearance models for discriminating all targets as well as a set of pairwise functions that are based on models for differentiating corresponding pairs of tracklets the online crf approach is more powerful at distinguishing spatially close targets with similar appearances as well as in tracking targets in presence of camera motions an efficient algorithm is introduced for finding an association with low energy cost we present results on four public data sets and show significant improvements compared with several state of art methods

tracking in low frame rate video a cascade particle filter with discriminative observers of different life spans tracking object in low frame rate video or with abrupt motion poses two main difficulties which most conventional tracking methods can hardly handle 1 poor motion continuity and increased search space 2 fast appearance variation of target and more background clutter due to increased search space in this paper we address the problem from a view which integrates conventional tracking and detection and present a temporal probabilistic combination of discriminative observers of different lifespans each observer is learned from different ranges of samples with different subsets of features to achieve varying level of discriminative power at varying cost an efficient fusion and temporal inference is then done by a cascade particle filter which consists of multiple stages of importance sampling experiments show significantly improved accuracy of the proposed approach in comparison with existing tracking methods under the condition of low frame rate data and abrupt motion of both target and camera

tracking with occlusions via graph cuts this work presents a new method for tracking and segmenting along time interacting objects within an image sequence one major contribution of the paper is the formalization of the notion of visible and occluded parts for each object we aim at tracking these two parts assuming that the velocity of each object is driven by a dynamical law predictions can be used to guide the successive estimations separating these predicted areas into good and bad parts with respect to the final segmentation and representing the objects with their visible and occluded parts permit handling partial and complete occlusions to achieve this tracking a label is assigned to each object and an energy function representing the multilabel problem is minimized via a graph cuts optimization this energy contains terms based on image intensities which enable segmenting and regularizing the visible parts of the objects it also includes terms dedicated to the management of the occluded and disappearing areas which are defined on the areas of prediction of the objects the results on several challenging sequences prove the strength of the proposed approach

the perona malik paradox the perona malik equation is a formally ill posed parabolic equation for which simple discretizations are nevertheless numerically found to be stable after discussing the background of this parad

weak classifier for density estimation in eye localization and tracking in this paper we propose a fast weak classifier that can detect and track eyes in video sequences the approach relies on a least squares detector based on the inner product detector ipd that can stimate a probability density distribution for a feature s location which fits naturally with a bayesian estimation cycle such as a kalman or particle filter as a least squares sliding window detector it possesses tolerance to small variations in the desired pattern while maintaining good generalization capabilities and computational efficiency we propose two approaches to integrating the ipd with a particle filter tracker we use the bioid feret lfpw and cofw public datasets as well as five manually annotated high definition video sequences to quantitatively evaluate the algorithms performance the video data set contains four subjects different types of backgrounds blurring due to fast motion and occlusions all code and data are available

ensemble tracking we consider tracking as a binary classification problem where an ensemble of weak classifiers is trained online to distinguish between the object and the background the ensemble of weak classifiers is combined into a strong classifier using adaboost the strong classifier is then used to label pixels in the next frame as either belonging to the object or the background giving a confidence map the peak of the map and hence the new position of the object is found using mean shift temporal coherence is maintained by updating the ensemble with new weak classifiers that are trained online during tracking we show a realization of this method and demonstrate it on several video sequences

mode finding for mixtures of gaussian distributions gradient quadratic and fixed point iteration algorithms and appropriate values for their control parameters are derived for finding all modes of a gaussian mixture a problem with applications in clustering and regression the significance of the modes found is quantified locally by hessian based error bars and globally by the entropy as sparseness measure

stochastic relaxation gibbs distributions and the bayesian restoration of images we make an analogy between images and statistical mechanics systems pixel gray levels and the presence and orientation of edges are viewed as states of atoms or molecules in a lattice like physical system the assignment of an energy function in the physical system determines its gibbs distribution because of the gibbs distribution markov random field mrf equivalence this assignment also determines an mrf image model the energy function is a more convenient and natural mechanism for embodying picture attributes than are the local characteristics of the mrf for a range of degradation mechanisms including blurring nonlinear deformations and multiplicative or additive noise the posterior distribution is an mrf with a structure akin to the image model by the analogy the posterior distribution defines another imaginary physical system gradual temperature reduction in the physical system isolates low energy states annealing or what is the same thing the most probable states under the gibbs distribution the analogous operation under the posterior distribution yields the maximum a posteriori map estimate of the image given the degraded observations the result is a highly parallel relaxation algorithm for map estimation we establish convergence properties of the algorithm and we experiment with some simple pictures for which good restorations are obtained at low signal to noise ratios

a probabilistic exclusion principle for tracking multiple objects tracking multiple targets is a challenging problem especially when the targets are identical in the sense that the same model is used to describe each target in this case simply instantiating several independent 1 body trackers is not an adequate solution because the independent trackers tend to coalesce onto the best fitting target this paper presents an observation density for tracking which solves this problem by exhibiting a probabilistic exclusion principle exclusion arises naturally from a systematic derivation of the observation density without relying on heuristics another important contribution of the paper is the presentation of partitioned sampling a new sampling method for multiple object tracking partitioned sampling avoids the high computational load associated with fully coupled trackers while retaining the desirable properties of coupling

impsac synthesis of importance sampling and random sample consensus this paper proposes a new method for recovery of epipolar geometry and feature correspondence between images which have undergone a significant deformation either due to large rotation or wide baseline of the cameras the method also encodes the uncertainty by providing an arbitrarily close approximation to the posterior distribution of the two view relation the method operates on a pyramid from coarse to fine resolution thus raising the problem of how to propagate information from one level to another in a statistically consistent way the distribution of the parameters at each resolution is encoded nonparametrically as a set of particles at the coarsest level a random sample consensus monte carlo markov chain ransac mcmc estimator is used to initialize this set of particles the posterior can then be approximated as a mixture of gaussians fitted to these particles the distribution at a coarser level influences the distribution at a finer level using the technique of sampling importance resampling sir and mcmc which allows for asymptotically correct approximations of the posterior distribution the estimate of the posterior distribution at the level above is being used as the importance sampling function to generate a new set of particles which can be further improved by mcmc it is shown that the method is superior to previous single resolution ransac style feature matchers

tracking deforming objects using particle filtering for geometric active contours tracking deforming objects involves estimating the global motion of the object and its local deformations as a function of time tracking algorithms using kalman filters or particle filters have been proposed for finite dimensional representations of shape but these are dependent on the chosen parametrization and cannot handle changes in curve topology geometric active contours provide a framework which is parametrization independent and allow for changes in topology in the present work we formulate a particle filtering algorithm in the geometric active contour framework that can be used for tracking moving and deforming objects to the best of our knowledge this is the first attempt to implement an approximate particle filtering algorithm for tracking on a theoretically infinite dimensional state space

lie x depth image based articulated object pose estimation tracking and action recognition on lie groups pose estimation tracking and action recognition of articulated objects from depth images are important and challenging problems which are normally considered separately in this paper a unified paradigm based on lie group theory is proposed which enables us to collectively address these related problems our approach is also applicable to a wide range of articulated objects empirically it is evaluated on lab animals including mouse and fish as well as on human hand on these applications it is shown to deliver competitive results compared to the state of the arts and non trivial baselines including convolutional neural networks and regression forest methods moreover new sets of annotated depth data of articulated objects are created which together with our code are made publicly available

studentized dynamical system for robust object tracking this paper describes a studentized dynamical system sds for robust target tracking using a subspace representation dynamical systems ds provide a powerful framework for the probabilistic modeling of temporal sequences visual tracking problems are often cast as a sequential inference problem within the ds framework and a compact way to model the observation distributions i e object appearances is through probabilistic principal component analysis ppca ppca is a classic gaussian based subspace representation method and a popular tool for appearance modeling although gaussian density has theoretically nice properties resulting in models that are always tractable they are also severely limited in practical settings one of the central issues in the use of ppca for target appearance modeling is that it is very sensitive to outliers the gaussian density has a very light tail while real world data with outliers exhibit heavy tails recently more heavy tailed distributions e g student s t distribution have been introduced to increase the robustness of the original ppca we propose to augment the traditional target tracking ds by adding a set of auxiliary latent variables to adjust the shape of the observation distribution we show that by carefully choosing the probability density of these auxiliary latent variables a more robust observation distribution can be obtained with tails heavier than gaussian numerical experiments verify that the proposed sds has a better capability to handle considerable amount of outlier noise and an improved tracking performance over ds with a gaussian based observation model

kernel based object tracking a new approach toward target representation and localization the central component in visual tracking of nonrigid objects is proposed the feature histogram based target representations are regularized by spatial masking with an isotropic kernel the masking induces spatially smooth similarity functions suitable for gradient based optimization hence the target localization problem can be formulated using the basin of attraction of the local maxima we employ a metric derived from the bhattacharyya coefficient as similarity measure and use the mean shift procedure to perform the optimization in the presented tracking examples the new method successfully coped with camera motion partial occlusions clutter and target scale variations integration with motion filters and data association techniques is also discussed we describe only a few of the potential applications exploitation of background information kalman tracking using motion models and face tracking

bayesian modeling of dynamic scenes for object detection accurate detection of moving objects is an important precursor to stable tracking or recognition in this paper we present an object detection scheme that has three innovations over existing approaches first the model of the intensities of image pixels as independent random variables is challenged and it is asserted that useful correlation exists in intensities of spatially proximal pixels this correlation is exploited to sustain high levels of detection accuracy in the presence of dynamic backgrounds by using a nonparametric density estimation method over a joint domain range representation of image pixels multimodal spatial uncertainties and complex dependencies between the domain location and range color are directly modeled we propose a model of the background as a single probability density second temporal persistence is proposed as a detection criterion unlike previous approaches to object detection which detect objects by building adaptive models of the background the foregrounds modeled to augment the detection of objects without explicit tracking since objects detected in the preceding frame contain substantial evidence for detection in the current frame finally the background and foreground models are used competitively in a map mrf decision framework stressing spatial context as a condition of detecting interesting objects and the posterior function is maximized efficiently by finding the minimum cut of a capacitated graph experimental validation of the proposed method is performed and presented on a diverse set of dynamic scenes

a particle filtering framework for joint video tracking and pose estimation a method is introduced to track the object s motion and estimate its pose directly from 2 d image sequences scale invariant feature transform sift is used to extract corresponding feature points from image sequences we demonstrate that pose estimation from the corresponding feature points can be formed as a solution to sylvester s equation we show that the proposed approach to the solution of sylvester s equation is equivalent to the classical svd method for 3d 3d pose estimation however whereas classical svd cannot be used for pose estimation directly from 2 d image sequences our method based on sylvester s equation provides a new approach to pose estimation smooth video tracking and pose estimation is finally obtained by using the solution to sylvester s equation within the importance sampling density of the particle filtering framework finally computer simulation experiments conducted over synthetic data and real world videos demonstrate the effectiveness of our method in both robustness and speed compared with other similar object tracking and pose estimation methods

structure regularized compressive tracking with online data driven sampling being a powerful appearance model compressive random projection derives effective haar like features from non rotated 4 d parameterized rectangles thus supporting fast and reliable object tracking in this paper we show that such successful fast compressive tracking scheme can be further significantly improved by structural regularization and online data driven sampling our major contribution is threefold first we find that superpixel guided compressive projection can generate more discriminative features by sufficiently capturing rich local structural information of images second we propose fast directional integration that enables low cost extraction of feasible haar like features from arbitrarily rotated 5 d parameterized rectangles to realize more accurate object localization third beyond naive dense uniform sampling we present two practical online data driven sampling strategies to produce less yet more effective candidate and training samples for object detection and classifier updating respectively extensive experiments on real world benchmark data sets validate the superior performance i e much better object localization ability and robustness of the proposed approach over state of the art trackers

effective tracking through tree search a new contour tracking algorithm is presented tracking is posed as a matching problem between curves constructed out of edges in the image and some shape space describing the class of objects of interest the main contributions of the paper are to present an algorithm which solves this problem accurately and efficiently in a provable manner in particular the algorithm s efficiency derives from a novel tree search algorithm through the shape space which allows for much of the shape space to be explored with very little effort this latter property makes the algorithm effective in highly cluttered scenes as is demonstrated in an experimental comparison with a condensation tracker

map based stochastic diffusion for stereo matching and line fields estimation this paper proposes a stochastic approach to estimate the disparity field combined with line field in the maximum a posteriori map method based on markov random field mrf model it is important to optimize and converge the gibbs potential function corresponding to the perturbed disparity field the proposed optimization method stochastic diffusion takes advantage of the probabilistic distribution of the neighborhood fields to diffuse the gibbs potential space iteratively by using the neighborhood distribution in the non random and non deterministic diffusion both the estimation accuracy and the convergence speed are improved in the paper the hierarchical stochastic diffusion is also applied to the disparity field the hierarchical approach reduces the memory and computational load and increases the convergence speed of the potential space the paper also proposes an effective configuration of the neighborhood to be suitable for the hierarchical disparity structure according to the experiments the stochastic diffusion shows good estimation performance the line field improves the estimation at the object boundary and coincides with the object boundary with the useful contours the stochastic diffusion is applicable to any kind of field estimation given the appropriate definition of the field and mrf models

probabilistic detection and tracking of motion boundaries we propose a bayesian framework for representing and recognizing local image motion in terms of two basic models translational motion and motion boundaries motion boundaries are represented using a non linear generative model that explicitly encodes the orientation of the boundary the velocities on either side the motion of the occluding edge over time and the appearance disappearance of pixels at the boundary we represent the posterior probability distribution over the model parameters given the image data using discrete samples this distribution is propagated over time using a particle filtering algorithm to efficiently represent such a high dimensional space we initialize samples using the responses of a low level motion discontinuity detector the formulation and computational model provide a general probabilistic framework for motion estimation with multiple non linear models

dynamic active contours for visual tracking visual tracking using active contours is usually set in a static framework the active contour tracks the object of interest in a given frame of an image sequence a subsequent prediction step ensures good initial placement for the next frame this approach is unnatural the curve evolution gets decoupled from the actual dynamics of the objects to be tracked true dynamical approaches exist all being marker particle based and thus prone to the shortcomings of such particle based implementations in particular topological changes are not handled naturally in this framework the now classical level set approach is tailored for evolutions of manifolds of codimension one however dynamic curve evolution is at least a codimension two problem we propose an efficient level set based approach for dynamic curve evolution which addresses the artificial separation of segmentation and prediction while retaining all the desirable properties of the level set formulation it is based on a new energy minimization functional which for the first time puts dynamics into the geodesic active contour framework

an unconditionally stable numerical method for bimodal image segmentation in this paper we propose a new level set based model and an unconditionally stable numerical method for bimodal image segmentation our model is based on the lee seo active contour model the numerical scheme is semi implicit and solved by an analytical method the unconditional stability of the proposed numerical method is proved analytically we demonstrate performance of the proposed image segmentation algorithm on several synthetic and real images to confirm the efficiency and stability of the proposed method

filtering image sequences from a moving object and the edge detection problem in this paper we consider the problem of filtering sequences of images taken from moving objects with the aim of recovering information about the object itself as well as its underlying motion we first provide a formal description of the admissible classes of images and possibly nonrigid motions and of the functional relationship between the original image and the observed one blurring and noisy effects we then focus on the problem of edge detection assuming full information about the motion we propose a procedure that includes a preliminary preprocessing of the measured image aimed to localize the detection problem and to improve the signal to noise ratio then the edge identification is accomplished by an algorithm which implements recursive linear quadratic estimation and hypothesis testing finally the procedure is tested against simulated and real data

conditional mean estimation via jump diffusion processes in multiple target tracking recognition a new algorithm is presented for generating the conditional mean estimates of functions of target positions orientations and type in recognition and tracking of an unknown number of targets and target types taking a bayesian approach a posterior measure is defined on the tracking target parameter space by combining a narrowband sensor array manifold model with a high resolution imaging model and a prior based on airplane dynamics the newtonian force equations governing rigid body dynamics are utilized to form the prior density on airplane motion the conditional mean estimates are generated using a random sampling algorithm based on jump diffusion processes for empirically generating mmse estimates of functions of these random target positions orientations and type under the posterior measure results are presented on target tracking and identification from an implementation of the algorithm on a networked silicon graphics workstation and decmpp maspar parallel machine

bayesian neighborhood component analysis learning a distance metric in feature space potentially improves the performance of the k nearest neighbor classifier and is useful in many real world applications many metric learning ml algorithms are however based on the point estimation of a quadratic optimization problem which is time consuming susceptible to overfitting and lacks a natural mechanism to reason with parameter uncertainty a property useful especially when the training set is small and or noisy to deal with these issues we present a novel bayesian ml bml method called bayesian neighborhood component analysis nca based on the well known nca method in which the metric posterior is characterized by the local label consistency constraints of observations encoded with a similarity graph instead of independent pairwise constraints for efficient bayesian inference we explore the variational lower bound over the log likelihood of the original nca objective experiments on several publicly available data sets demonstrate that the proposed method is able to learn robust metric measures from small size data set and or from challenging training set with labels contaminated by errors the proposed method is also shown to outperform a previous pairwise constrained bml method

tracking by affine kernel transformations using color and boundary cues kernel based trackers aggregate image features within the support of a kernel a mask regardless of their spatial structure these trackers spatially fit the kernel usually in location and in scale such that a function of the aggregate is optimized we propose a kernel based visual tracker that exploits the constancy of color and the presence of color edges along the target boundary the tracker estimates the best affinity of a spatially aligned pair of kernels one of which is color related and the other of which is object boundary related in a sense this work extends previous kernel based trackers by incorporating the object boundary cue into the tracking process and by allowing the kernels to be affinely transformed instead of only translated and isotropically scaled these two extensions make for more precise target localization a more accurately localized target also facilitates safer updating of its reference color model further enhancing the tracker s robustness the improved tracking is demonstrated for several challenging image sequences

sequential monte carlo methods for multiple target tracking and data fusion the classical particle filter deals with the estimation of one state process conditioned on a realization of one observation process we extend it here to the estimation of multiple state processes given realizations of several kinds of observation processes the new algorithm is used to track with success multiple targets in a bearings only context whereas a jpdaf diverges making use of the ability of the particle filter to mix different types of observations we then investigate how to join passive and active measurements for improved tracking

semi implicit finite volume scheme for solving nonlinear diffusion equations in image processing we propose and prove a convergence of the semi implicit finite volume approximation scheme for the numerical solution of the modified in the sense of catte lions morel and coll perona malik nonlinear image selective smoothing equation called anisotropic diffusion in the image processing the proof is based on l 2 a priori estimates and kolmogorov s compactness theorem the implementation aspects and computational results are discussed

nonlinear image filtering with edge and corner enhancement the authors describe a model of nonlinear image filtering for noise reduction and edge enhancement using anisotropic diffusion the method is designed to enhance not only edges but corners and t junctions as well the method roughly corresponds to a nonlinear diffusion process with backward heat flow across the strong edges such a process is ill posed making the results depend strongly on how the algorithm differs from the diffusion process two ways of modifying the equations using simulations on a variable grid are studied

recognition by linear combinations of models an approach to visual object recognition in which a 3d object is represented by the linear combination of 2d images of the object is proposed it is shown that for objects with sharp edges as well as with smooth bounding contours the set of possible images of a given object is embedded in a linear space spanned by a small number of views for objects with sharp edges the linear combination representation is exact for objects with smooth boundaries it is an approximation that often holds over a wide range of viewing angles rigid transformations with or without scaling can be distinguished from more general linear transformations of the object by testing certain constraints placed on the coefficients of the linear combinations three alternative methods of determining the transformation that matches a model to a given image are proposed

exploiting hierarchical dense structures on hypergraphs for multi object tracking most multi object tracking algorithms are developed within the tracking by detection framework that consider the pairwise appearance similarities between detection responses or tracklets within a limited temporal window and thus less effective in handling long term occlusions or distinguishing spatially close targets with similar appearance in crowded scenes in this work we propose an algorithm that formulates the multi object tracking task as one to exploit hierarchical dense structures on an undirected hypergraph constructed based on tracklet affinity the dense structures indicate a group of vertices that are inter connected with a set of hyperedges with high affinity values the appearance and motion similarities among multiple tracklets across the spatio temporal domain are considered globally by exploiting high order similarities rather than pairwise ones thereby facilitating distinguish spatially close targets with similar appearance in addition the hierarchical design of the optimization process helps the proposed tracking algorithm handle long term occlusions robustly extensive experiments on various challenging datasets of both multi pedestrian and multi face tracking tasks demonstrate that the proposed algorithm performs favorably against the state of the art methods

self expressive tracking target representation is critical to visual tracking a good representation usually exploits some inherent relationship and structures among the observed targets the candidates or both in this work we observe that the candidates are strongly correlated to each other and exhibit obvious clustering structure when they are densely sampled around possible target locations thus we propose a self expressive tracking set algorithm based on an accurate representation with good discriminative performance the interrelationship and the clustering structure among the observed targets and the candidates are exploited by using a self expressive scheme with a low rank constraint further we design a discriminative criterion of the likelihood for target location which simultaneously considers the target background and representation errors to appropriately capture the appearance changes of the target we develop an update strategy that adaptively switches different update rates during tracking extensive experiments demonstrate that our tracking algorithm outperforms many other state of the art methods highlightsan observation of the inherent relationship and structure among the candidates a structured representation with naturally discriminative power for target description an evaluation criterion of the likelihood of the candidates belonging to the target an update strategy with adaptive update rates for appearance model maintenance

deform pf mt particle filter with mode tracker for tracking nonaffine contour deformations we propose algorithms for tracking the boundary contour of a deforming object from an image sequence when the nonaffine local deformation over consecutive frames is large and there is overlapping clutter occlusions low contrast or outlier imagery when the object is arbitrarily deforming each or at least most contour points can move independently contour deformation then forms an infinite in practice very large dimensional space direct application of particle filters pf for large dimensional problems is impractically expensive however in most real problems at any given time most of the contour deformation occurs in a small number of dimensions effective basis space while the residual deformation in the rest of the state space residual space is small this property enables us to apply the particle filtering with mode tracking pf mt idea that was proposed for such large dimensional problems in recent work since most contour deformation is low spatial frequency we propose to use the space of deformation at a subsampled set of locations as the effective basis space the resulting algorithm is called deform pf mt it requires significant modifications compared to the original pf mt because the space of contours is a non euclidean infinite dimensional space

a bayesian approach to dynamic contours through stochastic sampling and simulated annealing in many applications of image analysis simply connected objects are to be located in noisy images during the last 5 6 years active contour models have become popular for finding the contours of such objects connected to these models are iterative algorithms for finding the minimizing energy curves making the curves behave dynamically through the iterations these approaches do however have several disadvantages the numerical algorithms that are in use constrain the models that can be used furthermore in many cases only local minima can be achieved in this paper the author discusses a method for curve detection based on a fully bayesian approach a model for image contours which allows the number of nodes on the contours to vary is introduced iterative algorithms based on stochastic sampling is constructed which make it possible to simulate samples from the posterior distribution making estimates and uncertainty measures of specific quantities available further simulated annealing schemes making the curve move dynamically towards the global minimum energy configuration are presented in theory no restrictions on the models are made in practice however computational aspects must be taken into consideration when choosing the models much more general models than the one used for active contours may however be applied the approach is applied to ultrasound images of the left ventricle and to magnetic resonance images of the human brain and show promising results

contour grouping based on contour skeleton duality in this paper we present a method for grouping relevant object contours in edge maps by taking advantage of contour skeleton duality regularizing contours and skeletons simultaneously allows us to combine both low level perceptual constraints as well as higher level model constraints in a very effective way the models are represented using paths in symmetry sets skeletons are treated as trajectories of an imaginary virtual robot in a discrete space of symmetric points obtained from pairs of edge segments boundaries are then defined as the maps obtained by grouping the associated pairs of edge segments along the trajectories casting the grouping problem in this manner makes it similar to the problem of simultaneous localization and mapping slam hence we adapt the state of the art probabilistic framework namely rao blackwellized particle filtering that has been successfully applied to slam we use the framework to maximize the joint posterior over skeletons and contours

the contracting curve density algorithm fitting parametric curve models to images using local self adapting separation criteria the task of fitting parametric curve models to the boundaries of perceptually meaningful image regions is a key problem in computer vision with numerous applications such as image segmentation pose estimation object tracking and 3 d reconstruction in this article we propose the contracting curve density ccd algorithm as a solution to the curve fitting problem the ccd algorithm extends the state of the art in two important ways first it applies a novel likelihood function for the assessment of a fit between the curve model and the image data this likelihood function can cope with highly inhomogeneous image regions because it is formulated in terms of local image statistics the local image statistics are learned on the fly from the vicinity of the expected curve they provide therefore locally adapted criteria for separating the adjacent image regions these local criteria replace often used predefined fixed criteria that rely on homogeneous image regions or specific edge properties the second contribution is the use of blurred curve models as efficient means for iteratively optimizing the posterior density over possible model parameters these blurred curve models enable the algorithm to trade off two conflicting objectives namely heaving a large area of convergence and achieving high accuracy we apply the ccd algorithm to several challenging image segmentation and 3 d pose estimation problems our experiments with rgb images show that the ccd algorithm achieves a high level of robustness and sub pixel accuracy even in the presence of severe texture shading clutter partial occlusion and strong changes of illumination

bittracker a bitmap tracker for visual tracking under very general conditions this paper addresses the problem of visual tracking under very general conditions a possibly non rigid target whose appearance may drastically change over time general camera motion a 3d scene and no a priori information except initialization this is in contrast to the vast majority of trackers which rely on some limited model in which for example the target s appearance is known a priori or restricted the scene is planar or a pan tilt zoom camera is used their goal is to achieve speed and robustness but their limited context may cause them to fail in the more general case the proposed tracker works by approximating in each frame a pdf probability distribution function of the target s bitmap and then estimating the maximum a posteriori bitmap the pdf is marginalized over all possible motions per pixel thus avoiding the stage in which optical flow is determined this is an advantage over other general context trackers that do not use the motion cue at all or rely on the error prone calculation of optical flow using a gibbs distribution with respect to the first order neighborhood system yields a bitmap pdf whose maximization may be transformed into that of a quadratic pseudo boolean function the maximum of which is approximated via a reduction to a maximum flow problem many experiments were conducted to demonstrate that the tracker is able to track under the aforementioned general context

particle filter with a mode tracker for visual tracking across illumination changes in this correspondence our goal is to develop a visual tracking algorithm that is able to track moving objects in the presence of illumination variations in the scene and that is robust to occlusions we treat the illumination and motion x y translation and scale parameters as the unknown state sequence the observation is the entire image and the observation model allows for occasional occlusions modeled as outliers the nonlinearity and multimodality of the observation model necessitate the use of a particle filter pf due to the inclusion of illumination parameters the state dimension increases thus making regular pfs impractically expensive we show that the recently proposed approach using a pf with a mode tracker can be used here since even in most occlusion cases the posterior of illumination conditioned on motion and the previous state is unimodal and quite narrow the key idea is to importance sample on the motion states while approximating importance sampling by posterior mode tracking for estimating illumination experiments demonstrate the advantage of the proposed algorithm over existing pf based approaches for various face and vehicle tracking we are also able to detect illumination model changes e g those due to transition from shadow to sunlight or vice versa by using the generalized expected log likelihood statistics and successfully compensate for it without ever loosing track

efficient simplicial reconstructions of manifolds from their samples an algorithm for manifold learning is presented given only samples of a finite dimensional differentiable manifold and no a priori knowledge of the manifold s geometry or topology except for its dimension the goal is to find a description of the manifold the learned manifold must approximate the true manifold well both geometrically and topologically when the sampling density is sufficiently high the proposed algorithm constructs a simplicial complex based on approximations to the tangent bundle of the manifold an important property of the algorithm is that its complexity depends on the dimension of the manifold rather than that of the embedding space successful examples are presented in the cases of learning curves in the plane curves in space and surfaces in space in addition a case when the algorithm fails is analyzed

an evolutionary particle filter with the immune genetic algorithm for intelligent video target tracking particle filter algorithm is widely used for target tracking using video sequences which is of great importance for intelligent surveillance applications however there is still much room for improvement e g the so called sample impoverishment it is brought by re sampling which aims to avoid particle degradation and thus becomes the inherent shortcoming of the particle filter in order to solve the problem of sample impoverishment increase the number of meaningful particles and ensure the diversity of the particle set an evolutionary particle filter with the immune genetic algorithm iga for target tracking is proposed by adding iga in front of the re sampling process to increase particle diversity particles are regarded as the antibodies of the immune system and the state of target being tracked is regarded as the external invading antigen with the crossover and mutation process the immune system produces a large number of new antibodies particles and thus the new particles can better approximate the true state by exploiting new areas regulatory mechanisms of antibodies such as promotion and suppression ensure the diversity of the particle set in the proposed algorithm the particle set optimized by iga can better express the true state of the target and the number of meaningful particles can be increased significantly the effectiveness and robustness of the proposed particle filter are verified by target tracking experiments simulation results show that the proposed particle filter is better than the standard one in particle diversity and efficiency the proposed algorithm can easily be extended to multiple objects tracking problems with occlusions

shape tracking with occlusions via coarse to fine region based sobolev descent we present a method to track the shape of an object from video the method uses a joint shape and appearance model of the object which is propagated to match shape and radiance in subsequent frames determining object shape self occlusions and dis occlusions of the object from camera and object motion pose difficulties to joint shape and appearance models in tracking they are unable to adapt to new shape and appearance information leading to inaccurate shape detection in this work we model self occlusions and dis occlusions in a joint shape and appearance tracking framework self occlusions and the warp to propagate the model are coupled thus we formulate a joint optimization problem we derive a coarse to fine optimization method advantageous in tracking that initially perturbs the model by coarse perturbations before transitioning to finer scale perturbations seamlessly this coarse to fine behavior is automatically induced by gradient descent on a novel infinite dimensional riemannian manifold that we introduce the manifold consists of planar parameterized regions and the metric that we introduce is a novel sobolev metric experiments on video exhibiting occlusions dis occlusions complex radiance and background show that occlusion dis occlusion modeling leads to superior shape accuracy

differential earth mover s distance with its applications to visual tracking the earth mover s distance emd is a similarity measure that captures perceptual difference between two distributions its computational complexity however prevents a direct use in many applications this paper proposes a novel differential emd demd algorithm based on the sensitivity analysis of the simplex method and offers a speedup at orders of magnitude compared with its brute force counterparts the demd algorithm is discussed and empirically verified in the visual tracking context the deformations of the distributions for objects at different time instances are accommodated well by the emd and the differential algorithm makes the use of emd in real time tracking possible to further reduce the computation signatures i e variable size descriptions of distributions are employed as an object representation the new algorithm models and estimates local background scenes as well as foreground objects to handle scale changes in a principled way extensive quantitative evaluation of the proposed algorithm has been carried out using benchmark sequences and the improvement over the standard mean shift tracker is demonstrated

learning to track the visual motion of contours abstract a development of a method for tracking visual contours is described given an untrained tracker a training motion of an object can be observed over some extended time and stored as an image sequence the image sequence is used to learn parameters in a stochastic differential equation model these are used in turn to build a tracker whose predictor imitates the motion in the training set tests show that the resulting trackers can be markedly tuned to desired curve shapes and classes of motions

robust tracking with discriminative ranking lists in this paper we propose a novel tracking algorithm i e the discriminative ranking list based tracker drltracker the drltracker models the target object and its local background by using ranking lists of patches of different scales within object bounding boxes the ranking list of each of such patches is its k nearest neighbors patches of the same scale with ranking lists of high purity values meaning high probabilities to be on the target object and some confusable background patches constitute the object model under that scale a pair of object models of two different scales collaborate to determine which patches may belong to the target object in the next frame the drltracker can effectively alleviate the distraction problem and its superior ability over several representative and state of the art trackers is demonstrated through extensive experiments

mean shift trackers with cross bin metrics cross bin metrics have been shown to be more suitable than bin by bin metrics for measuring the distance between histograms in various applications in particular a visual tracker that minimizes the earth mover s distance emd between the candidate and reference feature histograms has recently been proposed this tracker was shown to be more robust than the mean shift tracker which employs a bin by bin metric in each frame the former tracker iteratively shifts the candidate location by one pixel in the direction opposite to the emd s gradient until no improvement is made this optimization process involves the clustering of the candidate feature density in feature space as well as the computation of the emd between the candidate and reference feature histograms after each shift of the candidate location in this paper alternative trackers that employ cross bin metrics as well but that are based on mean shift ms iterations are derived the proposed trackers are simpler and faster due to 1 the use of ms based optimization which is not restricted to single pixel shifts 2 abstention from any clustering of feature densities and 3 abstention from emd computations in multidimensional spaces

video tracking based on sequential particle filtering on graphs in this paper we develop a novel solution for particle filtering on general graphs we provide an exact solution for particle filtering on directed cycle free graphs the proposed approach relies on a partial order relation in an antichain decomposition that forms a high order markov chain over the partitioned graph we subsequently derive a closed form sequential updating scheme for conditional density propagation using particle filtering on directed cycle free graphs we also provide an approximate solution for particle filtering on general graphs by splitting graphs with cycles into multiple directed cycle free subgraphs we then use the sequential updating scheme by alternating among the directed cycle free subgraphs to obtain an estimate of the density propagation we rely on the proposed method for particle filtering on general graphs for two video tracking applications 1 object tracking using high order markov chains and 2 distributed multiple object tracking based on multi object graphical interaction models experimental results demonstrate the improved performance of the proposed approach to particle filtering on graphs compared with existing methods for video tracking