special preparation of subjects or restrictive assumptions. The principal contribution of this paper is the development of a modified particle filter for search in high dimensional configuration spaces. It uses a continuation principle, based on annealing, to introduce the influence of narrow peaks

Abstract. We present an approach for human body parts tracking in 3D with prelearned motion models using multiple cameras. Gaussian Process Annealing Particle Filter is proposed for tracking in order to reduce the dimensionality of the problem and to increase the tracker’s stability and robustness

Tracking human pose using observations from less than three cameras is a challenging task due to ambiguity in the available image evidence. This work presents a method for tracking using a pre-trained model of activity to guide sampling within an Annealed Particle Filtering framework. The approach

vision, they are commonly known as particle filters. Simulated annealing, on the other hand, is a global optimization method derived from statistical mechanics. A recent heuristic approach to fuse these two techniques for motion capturing has become known as annealed particle filter. In order to analyze

-body tracking provides, such as action recognition or multi-person tracking. Hence this paper evaluates the behaviour of the annealed particle filter (APF – a widely-cited body tracking algorithm) with the goal of identifying and resolving the cause of failures in tracking under realistic conditions. A loose

of classical algorithms that increases exponentially with the dimension of the vectors' state becomes too difficult to handle. To tackle this problem, we propose a new approach that combines several annealing particle filters defined independently for each limb and belief propagation method to add

towards analyzing these data. An annealed particle filtering scheme where every particle encodes an instance of the pose of the HBM is employed. Likeli-hood between particles and input data is performed using occupancy and surface information and kinematic constrains are imposed in the propagation step

Abstract We propose a parallelized Annealed Particle Filter method via heterogeneous computing (P-APF), to implement real-time marker-less motion tracking based on OpenCL framework. The overall computing procedure in P-APF is decomposed into several computational tasks with corresponding

lehment, arsic, rigoll @ tum.de Current experiments with HCIs have shown a high demand for more natural interaction paradigms. Gestures are thereby considered the most important cue besides speech. In order to recognize gestures it is necessary to extract meaningful motion features from the body. Up to now mostly marker based tracking systems are used in virtual reality environments, since these were traditionally more reliable than purely image based detection methods. However, markers tend to be distracting and cumbersome. Following recent advances in processing power, it becomes possible to use a camera system in order to obtain a depth image of the test subject, match it to a pre-defined body model, and thus track the body parts over time. We will present a fullbody system based on APF which enables full body tracking utilizing point clouds recorded with a 3D sensor. Further refinement is provided by a specially adapted inverse kinematics system. A GPU based implementation speeds up processing significantly and allows near real time performance. 1.

of storage costs as well as for rapid adaptation to changing signal characteristics. In this paper, we review both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters. Particle filters are sequential Monte Carlo methods based on point mass