Opto‐electric target tracking algorithm based on local feature selection and particle filter optimization

Abstract

Summary Aiming at exploring the opto‐electric target tracking, which is an important technology in the field of computer vision, the binocular stereo vision camera opto‐electric target tracking is studied and and a multi feature fusion characterization modeling method locally weighted is proposed. The target area is divided into multiple sub‐image areas by the modeling method, the feature histogram after the background weighting is extracted, and the sub‐image region is taken as a basic unit for adjusting the feature weight. The sub‐image area selected is regarded as the significant area, and the significant area is further extracted and fused in particle filter tracking algorithm. Then, the obtained significant are is conducted with color distribution processing. In the state prediction stage, the Mean Shift algorithm is applied to optimize each particle so that it converges to the optimal position. The experiment results showed that the multi feature fusion representation modeling method has better tracking accuracy and stability compared with the traditional fusion method and after the color distribution treatment; it has strong anti‐jamming for background effect. It is concluded that using Mean Shift algorithm for particle optimization can further strengthen the accurate tracking of the targets.

Modeling and Simulation of English Speech Rationality Optimization Recognition Based on Improved Particle Filter Algorithm

“…e authors of [3,4] improved the traditional particle filter algorithm and used the latest observation information in the importance sampling process to more accurately approximate the posterior probability density function. e authors of [5,6] proposed a resampling algorithm to solve the problem of particle degradation. e authors of [7][8][9] use particle filtering algorithm to solve the damage identification problem of structural systems.…”

Neighborhood rough set based multi‐label feature selection with label correlation

“…12,[19][20][21] Roughly, these methods can be divided into three categories: wrapper, 22,23 embedded, 24,25 and filter. 26,27 The wrapper approach relies on a predefined learning method to perform a heuristic search across all possible feature subsets. Although this method has high precision, the number of potential feature subsets obtained by this method is exponentially related to the time complexity, which makes the calculation cost relatively expensive.…”

“…To date, various multi‐label feature selection (MFS) methods have been proposed 12,19‐21 . Roughly, these methods can be divided into three categories: wrapper, 22,23 embedded, 24,25 and filter 26,27 . The wrapper approach relies on a predefined learning method to perform a heuristic search across all possible feature subsets.…”

Special issue on 2017 International Conference of Intelligence Computation and Evolutionary Computation (ICEC2017)

“…Based on this, it proposes target tracking algorithm based on local weighted combining with particle filter optimization (TFFLE). In the process of the experiment, it takes qualitative analysis, selects the Bhattacharyya similarity and tracking error as the performance indexes, and tests it to demonstrate that the algorithm is effective …”