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Automatic Detection on Appearance Changes using Local Moment Invariants

Published: 23 February 2019 Publication History

Abstract

A reliable appearance template has to extract a set of invariant features that holds the blueprint of the target image, regardless of being affected by translation, scale, rotation, skew, reflection, contrast and blur. Invariant features are superior in describing a number of visually distinctive and outstanding characteristics of a target but inadequate to identify the appearance changes of the target at a particular surveillance time. Thus, this paper aims to present an automatic detection approach to discover the rate of appearance changes in the target by exploiting the locality property of moment invariants. Unlike the existing local feature descriptors which usually extract the salient features randomly from the target, the proposed approach examines the entire target and subsequently reveals a suspicious region that contains some features that are drifting away from the original template. When the variations of the target's size, shape and orientation are corrupting the features, the reliability of the appearance template will gradually deteriorate and affect the object tracking process. Experiments are conducted to show that a selection of orthogonal moments is applicable to identify the appearance changes of the target by using the generalization relation between geometric monomials and orthogonal polynomial functions. Other than locality property, the proposed approach also preserves the capabilities of distinctiveness and invariance.

References

[1]
Sit, A. and Kihara, D., 2014. Comparison of image patches using local moment invariants. IEEE Transactions on Image Processing, 23(5), 2369--2379.
[2]
Tuytelaars, T. and Mikolajczyk, K., 2008. Local invariant feature detectors: a survey. Foundations and trends® in computer graphics and vision, 3(3), 177--280.
[3]
Mikolajczyk, K. and Schmid, C., 2005. A performance evaluation of local descriptors. IEEE transactions on pattern analysis and machine intelligence, 27(10), 1615--1630.
[4]
Ong, L.Y., Chong, C.W. and Besar, R., 2006, November. Scale invariants of three-dimensional legendre moments. In TENCON 2006. 2006 IEEE Region 10 Conference, 1--4.
[5]
Ann, G.H., Besar, R. and Abas, F.S., 2010. Rotational invariants for Tchebichef moments. IEICE Electronics Express, 7(9), 577--582.
[6]
Papakostas, G. A., Koulouriotis, D.E., Karakasis, E.G. and Tourassis, V.D. 2013. Moment-based local binary patterns: a novel descriptor for invariant pattern recognition applications. In Neurocomputing, 99, 358--371.
[7]
VOT, 2016. Visual Object Tracking. (Accessed on April 30, 2017) http://www.votchallenge.net/vot2016/.
[8]
Yin, Z. and Collins, R., 2007, June. On-the-fly object modeling while tracking. In Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference, 1--8.
[9]
Miksik, O. and Mikolajczyk, K., 2012, November. Evaluation of local detectors and descriptors for fast feature matching. In Pattern Recognition (ICPR), 2012 21st International Conference, 2681--2684.
[10]
Lim, H.Y. and Kang, D.S., 2011. Object tracking system using a VSW algorithm based on color and point features. EURASIP Journal on Advances in Signal Processing, 2011:60.

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  1. Automatic Detection on Appearance Changes using Local Moment Invariants

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    ICCAE 2019: Proceedings of the 2019 11th International Conference on Computer and Automation Engineering
    February 2019
    160 pages
    ISBN:9781450362870
    DOI:10.1145/3313991
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    • The University of Western Australia, Department of Electronic Engineering, University of Western Australia
    • University of Melbourne: University of Melbourne
    • Macquarie University-Sydney

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 23 February 2019

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    Author Tags

    1. geometric moments
    2. non-rigidity
    3. object tracking
    4. orthogonal moments
    5. partial occlusion

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