Pose Estimation in Noncontinuous Video Sequences Using Evolutionary Correlation Filtering

Abstract

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