Citation

Koo, Voon Chet and Lau, Siong Hoe and Ong, Lee Yeng (2019) Automatic Detection on Appearance Changes using Local Moment Invariants. ACM International Conference Proceeding Series. pp. 47-51. ISSN 2374-6769

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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.

Item Type:	Article
Uncontrolled Keywords:	Orthogonal, partial occlusion, nonrigidity, object tracking
Subjects:	Q Science > QA Mathematics > QA440-699 Geometry. Trigonometry. Topology
Divisions:	Faculty of Engineering and Technology (FET) Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	11 Jan 2022 02:52
Last Modified:	11 Jan 2022 02:52
URII:	http://shdl.mmu.edu.my/id/eprint/8956

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