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Mathematical Problems in Engineering
Volume 2018, Article ID 8284123, 8 pages
https://doi.org/10.1155/2018/8284123
Research Article

Reliable Recognition of Partially Occluded Objects with Correlation Filters

1Department of Mathematics, Chelyabinsk State University, Chelyabinsk, Russia
2Department of Computer Science, CICESE, Carretera Ensenada-Tijuana 3918, 22860 Ensenada, BC, Mexico
3Facultad de Ciencias, Universidad Autonoma de Baja California, Carretera Tijuana-Ensenada, No. 3917, 22860 Ensenada, BC, Mexico

Correspondence should be addressed to Vitaly Kober; xm.esecic@rebokv

Received 24 August 2017; Revised 5 March 2018; Accepted 27 March 2018; Published 14 May 2018

Academic Editor: Paolo Lonetti

Copyright © 2018 Alexey Ruchay 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

Design of conventional correlation filters requires explicit knowledge of the appearance and shape of a target object, so the performance of correlation filters is significantly affected by changes in the appearance of the object in the input scene. In particular, the performance of correlation filters worsens when objects to be recognized are partially occluded by other objects, and the input scene contains a cluttered background and noise. In this paper, we propose a new algorithm for the design of a system consisting of a set of adaptive correlation filters for recognition of partially occluded objects in noisy scenes. Since the input scene may contain different fragments of the target, false objects, and background to be rejected, the system is designed in such a manner to guarantee equally high correlation peaks corresponding to parts of the target in the scenes. The key points of the system are as follows: (i) it consists of a bank of composite optimum filters, which yield the best performance for different parts of the target; (ii) it includes a fragmentation of the target into a given number of parts in the training stage to provide equal intensity responses of the system for each part of the target. With the help of computer simulation, the performance of the proposed algorithm for recognition partially occluded objects is compared with that of common algorithms in terms of objective metrics.