Target Centroid Position Estimation of Phase-Path Volume Kalman Filtering

Fengjun Hu

doi:10.1155/2016/4879293

Abstract

For the problem of easily losing track target when obstacles appear in intelligent robot target tracking, this paper proposes a target tracking algorithm integrating reduced dimension optimal Kalman filtering algorithm based on phase-path volume integral with Camshift algorithm. After analyzing the defects of Camshift algorithm, compare the performance with the SIFT algorithm and Mean Shift algorithm, and Kalman filtering algorithm is used for fusion optimization aiming at the defects. Then aiming at the increasing amount of calculation in integrated algorithm, reduce dimension with the phase-path volume integral instead of the Gaussian integral in Kalman algorithm and reduce the number of sampling points in the filtering process without influencing the operational precision of the original algorithm. Finally set the target centroid position from the Camshift algorithm iteration as the observation value of the improved Kalman filtering algorithm to fix predictive value; thus to make optimal estimation of target centroid position and keep the target tracking so that the robot can understand the environmental scene and react in time correctly according to the changes. The experiments show that the improved algorithm proposed in this paper shows good performance in target tracking with obstructions and reduces the computational complexity of the algorithm through the dimension reduction.

Highlights

Robot moving target detection and tracking based on computer vision has become a hot spot of research scholars at home and abroad
In order to solve various problems related to the target tracking, many scholars have done a lot of innovative research work in the field, and, in view of the practical problems of the different areas, many different target tracking algorithms are proposed, which mainly concentrate in the SIFT algorithm, Mean Shift algorithm, and Camshift algorithm
Wang et al propose Camshift target tracking algorithm based on the frame difference and motion estimation, to some extent, the proposed algorithm can solve the problem of target occlusion and too fast movement, but when the background is complex and there are other moving targets, it cannot track well

Summary

Introduction

Robot moving target detection and tracking based on computer vision has become a hot spot of research scholars at home and abroad. The combination of Mean Shift algorithm and particle filtering has good robustness for tracking target process in the case of complex environment and changed background, but because of the high algorithm complexity of particle filtering, it is not ideal in real time [10]. Wang et al propose Camshift target tracking algorithm based on the frame difference and motion estimation, to some extent, the proposed algorithm can solve the problem of target occlusion and too fast movement, but when the background is complex and there are other moving targets, it cannot track well. Li and others improve the interested area extraction in Camshift algorithm with Canny filtering, making the acquisition of target color histogram resist the influence of noise, while the method is not effective when the background profile is taken into the detection range [14]. This paper proposes a target tracking algorithm integrating reduced dimension optimal Kalman filtering algorithm based on phase-path volume integral with Camshift algorithm, improving the existed efficiency in target tracking aiming at Camshift algorithm

Performance Analysis of Target Tracking Algorithm

Target Centroid Position Estimation

Algorithm Performance Test

Conclusion