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Target Centroid Position Estimation of Phase-Path Volume Kalman Filtering.pdf

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Target Centroid Position Estimation of Phase-Path Volume Kalman Filtering

Research Article Target Centroid Position Estimation of Phase-Path Volume Kalman Filtering Fengjun Hu Institute of Information Technology, Zhejiang Shuren University, Hangzhou, Zhejiang 310014, China Correspondence should be addressed to Fengjun Hu; jainism@ Received 26 December 2014; Revised 25 April 2015; Accepted 29 April 2015 Academic Editor: Stefania Campopiano Copyright ? 2016 Fengjun Hu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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