This paper proposes the use of a particle filter with embedded particle swarm optimization as an efficient and effective way of dealing with 3d model-based human body tracking. A particle swarm optimization algorithm is utilized in the particle filter to shift the particles toward more promising configurations of the human model. The algorithm is shown to be able of tracking full articulated bo...

We present a strategy for designing an α-β-η-θ filter, a fixed-gain moving-object tracking filter using position and velocity measurements. First, performance indices and stability conditions for the filter are analytically derived. Then, an optimal gain design strategy using these results is proposed and its relationship to the position-velocity-measured (PVM) Kalman filter is shown. Numerical...

This paper is based on the analysis and variation of multi target tracking algorithm which is based on kalman filter a digital filter. In this paper integrated random coefficient matrices kalman filtering is analysed with a mathematical model and used to reduce error and results are compared. KeywordsKalman Filtering, multi target tracking, integrated random coefficient matrices.

In this chapter, we discuss the problem of tracking objects without having to learn a model of the object’s appearance in advance (i.e., model-free tracking). Object tracking has many practical applications, which motivated continous developments in the field. A particularly successful tracking technique that has been recently proposed is the structured tracker called Struck. Struck uses suppor...

People tracking is an essential part for modern service robots. In this paper we compare three different Bayesian estimators to perform such task: Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Sampling Importance Resampling (SIR) Particle Filter. We give a brief explanation of each technique and describe the system implemented to perform people tracking with a mobile robot usi...

In this paper, we propose a simple and effective algorithm for frequency estimation and tracking under a harmonic frequency environment. The proposed filter structure contains only one adaptive coefficient and is very efficient for tracking the fundamental frequency of the periodical signal. Index terms Adaptive IIR Notch filter, harmonics, frequency estimation

While the main objective of adaptive Filter-and-Sum beamforming is to obtain an enhanced speech signal for subsequent processing like speech recognition, we show how speaker localization information can be derived from the filter coefficients. To increase localization accuracy, speaker tracking is performed by non-linear Bayesian state estimation, which is realized by sequential Monte Carlo met...

Tracking posterior estimates for problems with data association uncertainty is one of the big open problems in the literature on filtering and tracking. This paper presents a new filter for the on-line tracking of many individual objects with data association ambiguities. It tightly integrates the continuous aspects of the problem (locating the objects) with the discrete aspects (the data assoc...

Model-based vision allows the recovery and tracking of the 3D position and orientation of a known object from a sequence of images. A Kalman filter can be used to improve the tracking stability with three main benefits. Firstly it is an optimal filter in the least squares sense, with the added advantage that the physical dynamics and constraints of the tracking problem can easily be built into ...