Multi-feature Fusion Tracking Based on A New Particle Filter

Abstract

A new kind of particle filter is proposed for the state estimation of nonlinear system. The proposed algorithm based on Quadrature Kalman Filter by using integral pruning factor, which optimizes and reorganizes the integration point. New algorithm overcomes the particle degeneration phenomenon well by using Pruning Quadrature Kalman Filter to produce optimized proposal distribution function. In the improving particle filter framework, using color and motion edge character as observation model. Fusing feature weights through the D-S evidence theory, and effectively avoid the questions of bad robust produced by the single color feature in the illumination of mutation, posture change and similar feature occlusion. Experiment results indicate that the proposed method is more robust to track object and has good performance in complex scene.

Exponential Stability of Impulsive Discrete Systems with Multiple Delays

“…For the exponential stability conditions of (11), we have the following corollary. Next, we will establish another new stability criterion for the impulsive delayed discrete system (11).…”

“…In recent decades, stability analysis of delayed continuous or discrete systems has attracted much attention, see, for example, [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] and the references therein. In many evolutionary systems there are two common phenomena: delay effects and impulsive effects.…”

Particle Filter Algorithm for Object Tracking Based on Color Local Entropy

“…To verify the accuracy and robustness of the proposed algorithm (CLEPF), considering object occlusion, illumination variation, small camera vibration, and some other factors, three typical comparison experiments have been designed to test the proposed algorithm and compared with three representative tracking method: color histogram [7], spatiogram [8], color and edge histogram [14]. In the end, we evaluate and compare the algorithms' quantitative performance and empirically demonstrate the accuracy of the proposed method.…”

“…These algorithms above improved the color feature performance in some degree but did not consider the influence of the illumination. For this reason, many scholars have proposed multiple cues tracking method, such as two features fusion: color fused with texture [12], or edge [13,14], sift [15], and motion [16], and more than two features fusion: color fused with edge, texture, regional gray feature, and so on [17,18]. Multiple cues fusion method improves the robustness of object model and overcomes the instability brought by using a single measurement source, but the extraction of a variety of feature and the calculation of the fusion strategy can lead to large sample space, heavy calculation burden.…”

Particle Filter-Based Object Tracking Using Joint Features of Color and Local Binary Pattern Histogram Fourier

“…In particle filter, the propagation of particle is performed by sample propagation through system model S k [9][18] [19]:…”