Tracking Articulated Body by Dynamic Markov Network

In recent years Sequential Monte Carlo (SMC) algorithms have been applied to capture the motion of humans. In this paper we apply a SMC algorithm to capture the motion of an articulated chain, e.g., a human arm, and show how the SMC algorithm can be improved in this context by apply- ing auxiliary information. In parallel to a model-based ap- proach we detect skin color blobs in the image as our aux- iliary information and find the probabilities of each blob representing the hand. The probabilities of these blobs are used to control the drawing of particles in the SMC algo- rithm and to correct the predicted particles. The approach is tested against the standard SMC algorithm and we find that our approach improve the standard SMC algorithm.

2008

In this paper, we discard the first-order Markov state-space model commonly used in visual tracking and present a framework of visual tracking using high-order Monte Carlo Markov chain. By using graphic models to obtain the conditional independence properties, we derive the general expression of posterior density function for the mth-order hidden Markov model. We subsequently use Sequential Importance Sampling method to estimate the posterior density and obtain the high-order particle filtering algorithm for tracking. Experimental results show the superior performance of our proposed algorithm to traditional first-order particle filtering tracking algorithm, i.e. particle filtering derived based on first-order Markov chain.

Particle Filter -ArPF-, which has been specifically designed for an efficient sampling of hierarchical spaces, generated by articulated objects. Our approach decomposes the articulated motion into layers for efficiency purposes, making use of a careful modeling of the diffusion noise along with its propagation through the articulations. This produces an increase of accuracy and prevent for divergences. The algorithm is tested on hand tracking due to its complex hierarchical articulated nature. With this purpose, a new dataset generation tool for quantitative evaluation is also presented in this paper.

Tracking of the upper human body is one of the most interesting and challenging research fields in computer vision and comprises an important component used in gesture recognition applications. In this paper a probabilistic approach towards arm and hand tracking is presented. We propose the use of a kinematics model together with a segmentation of the parameter space to cope with the space dimensionality problem. Moreover, the combination of particle filters with hidden Markov models enables the simultaneous tracking of several hypotheses for the body orientation and the configuration of each of the arms.

2009 Workshop on Motion and Video Computing (WMVC), 2009

Efficient monocular human pose tracking in dynamic scenes is an important problem. Existing pose tracking methods either use activity priors to restrict the search space, or use generative body models with weak kinematic constraints to infer pose over multiple frames; these often tends to be slow. We develop an efficient algorithm to track human pose by estimating multi-frame body dynamics without activity priors. We present a monte-carlo approximation of the body dynamics using spatio-temporal distributions over part tracks. To obtain tracks that favor kinematically feasible body poses, we propose a novel "kinematically constrained" particle filtering approach which results in more accurate pose tracking than other stochastic approaches that use single frame priors. We demonstrate the effectiveness of our approach on videos with actors performing various actions in indoor dynamic scenes.

Series in Machine Perception and Artificial Intelligence, 2009

Visual tracking of articulated motion is a complex task with high computational costs. Because of the fact that articulated objects are usually represented as a set of linked limbs, tracking is performed with the support of a model. Model-based tracking allows determining object pose in an effortless way and handling occlusions. However, the use of articulated models generates a multidimensional state-space and, therefore, the tracking becomes computationally very expensive or even infeasible.

2007

Abstract In this paper, we present two new articulated motion analysis and object tracking approaches: the decentralized articulated object tracking method and the hierarchical articulated object tracking method. The first approach avoids the common practice of using a high-dimensional joint state representation for articulated object tracking. Instead, we introduce a decentralized scheme and model the interpart interaction within an innovative Bayesian framework.

IEEE Transactions on Image Processing, 2000

Particle filtering (PF) is now established as one of the most popular methods for visual tracking. Within this framework, two assumptions are generally made. The first is that the data are temporally independent given the sequence of object states, and the second one is the use of the transition prior as proposal distribution. In this paper, we argue that the first assumption does not strictly hold and that the second can be improved. We propose to handle both modeling issues using motion. Explicit motion measurements are used to drive the sampling process towards the new interesting regions of the image, while implicit motion measurements are introduced in the likelihood evaluation to model the data correlation term. The proposed model allows to handle abrupt motion changes and to filter out visual distractors when tracking objects with generic models based on shape representations. Experimental results compared against the CONDENSATION algorithm have demonstrated superior tracking performance.

Lecture Notes in Computer Science, 2007

We present a probabilistic framework for component-based automatic detection and tracking of objects in video. We represent objects as spatio-temporal two-layer graphical models, where each node corresponds to an object or component of an object at a given time, and the edges correspond to learned spatial and temporal constraints. Object detection and tracking is formulated as inference over a directed loopy graph, and is solved with non-parametric belief propagation. This type of object model allows object-detection to make use of temporal consistency (over an arbitrarily sized temporal window), and facilitates robust tracking of the object. The two layer structure of the graphical model allows inference over the entire object as well as individual components. AdaBoost detectors are used to define the likelihood and form proposal distributions for components. Proposal distributions provide 'bottomup' information that is incorporated into the inference process, enabling automatic object detection and tracking. We illustrate our method by detecting and tracking two classes of objects, vehicles and pedestrians, in video sequences collected using a single grayscale uncalibrated carmounted moving camera.

1999

The human figure exhibits complex and rich dynamic behavior that is both nonlinear and time-varying. However, most work on tracking and synthesizing figure motion has employed either simple, generic dynamic models or highly specific hand-tailored ones.

2012

Abstract A general framework for sequential particle filtering on graphs is presented in this paper. We present two new articulated motion analysis and object tracking approaches: the graph-based sequential particle filtering framework for articulated object tracking and its hierarchical counterpart. Specifically, we estimate the interaction density by an efficient decomposed inter-part interaction model.

2009

We study articulated human tracking by combining spatial and temporal priors in an integrated online learning and inference framework, where body parts can be localized and segmented simultaneously. The temporal prior is represented by the motion trajectory in a low dimensional latent space learned from tracking history, and it predicts the configuration of each body part for the next frame. The spatial prior is encoded by a star-structured graphical model and embedded in the temporal prior, and it can be constructed "on-the-fly" from the predicted pose and used to evaluate and correct the prediction by assembling part detection results. Both temporal and spatial priors can be online learned incrementally through the Back Constrained-Gaussian Process Latent Variable Model (BC-GPLVM) that involves a temporal sliding window for online learning. Experiments show that the proposed algorithm can achieve accurate and robust tracking results for different walking subjects with significant appearance and motion variability.

2008 19th International Conference on Pattern Recognition, 2008

This paper presents an approach to human motion tracking using multiple pre-trained activity models for propagation of particles in Annealed Particle Filtering. Hidden Markov models are trained on dimensionally reduced joint angle data to produce models of activity. Particles are divided between models for propagation by HMM synthesis, before converging on a solution during the annealing process. The approach facilitates multi-view tracking of unknown subjects performing multiple known activities with low particle numbers.

Image and Vision …, 2007

This paper presents visual cues for object tracking in video sequences using particle filtering. A consistent histogram-based framework is developed for the analysis of colour, edge and texture cues. The visual models for the cues are learnt from the first frame and the tracking can be carried out using one or more of the cues. A method for online estimation of the noise parameters of the visual models is presented along with a method for adaptively weighting the cues when multiple models are used. A particle filter (PF) is designed for object tracking based on multiple cues with adaptive parameters. Its performance is investigated and evaluated with synthetic and natural sequences and compared with the mean-shift tracker. We show that tracking with multiple weighted cues provides more reliable performance than single cue tracking. (P. Brasnett), mila. [email protected] (L. Mihaylova). www.elsevier.com/locate/imavis Image and Vision Computing xxx (2006) xxx-xxx ARTICLE IN PRESS Please cite this article as: Paul Brasnett et al., Sequential Monte Carlo tracking by fusing multiple cues .

2008

This paper presents a Markov Chain Monte Carlo (MCMC) based particle filter to track multiple persons dedicated to video surveillance applications. This hybrid tracker, devoted to networked intelligent cameras, takes benefit from the best properties of both MCMC and joint particle filter. A saliency map-based proposal distribution is shown to limit the well-known burst in terms of particles and MCMC iterations. Qualitative and quantitative results for real-world video data are presented.