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Mathematical Problems in Engineering
Volume 2018, Article ID 5798696, 13 pages
https://doi.org/10.1155/2018/5798696
Research Article

Pose Estimation in Noncontinuous Video Sequences Using Evolutionary Correlation Filtering

1CETYS Universidad, Centro de Innovación y Diseño (CEID), Ave. CETYS Universidad No. 4, El Lago, 22210, Tijuana, BC, Mexico
2Instituto Politécnico Nacional, CITEDI-IPN, Ave. Instituto Politécnico Nacional 1310, Tijuana 22435, BC, Mexico

Correspondence should be addressed to Kenia Picos; xm.sytec@socip.ainek

Received 27 May 2018; Revised 24 September 2018; Accepted 18 October 2018; Published 31 October 2018

Academic Editor: Ioannis Kostavelis

Copyright © 2018 Kenia Picos et al. 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.

Abstract

In this paper, we propose an evolutionary correlation filtering approach for solving pose estimation in noncontinuous video sequences. The proposed algorithm computes the linear correlation between the input scene containing a target in an unknown environment and a bank of matched filters constructed from multiple views of the target and estimates of statistical parameters of the scene. An evolutionary approach for finding the optimal filter that produces the highest matching score in the correlator is implemented. The parameters of the filter bank evolve through generations to refine the quality of pose estimation. The obtained results demonstrate the robustness of the proposed algorithm in challenging image conditions such as noise, cluttered background, abrupt pose changes, and motion blur. The performance of the proposed algorithm yields high accuracy in terms of objective metrics for pose estimation in noncontinuous video sequences.