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Local feature-based multi-object recognition scheme for surveillance

Authors
Kim, DaehoonRho, SeungminHwang, Eenjun
Issue Date
10월-2012
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Keywords
Object recognition; SURF; Local feature; Feature descriptor; Surveillance
Citation
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, v.25, no.7, pp.1373 - 1380
Indexed
SCIE
SCOPUS
Journal Title
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
Volume
25
Number
7
Start Page
1373
End Page
1380
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/107361
DOI
10.1016/j.engappai.2012.03.005
ISSN
0952-1976
Abstract
In this paper, we propose an efficient multi-object recognition scheme for surveillance based on interest points of objects and their feature descriptors. In this scheme, we first define a set of object types of interest and collect their sample images. For each sample image, we detect interest points and construct their feature descriptors using SURF. Next, we perform a statistical analysis of the local features to select representative points among them. Intuitively, the representative points of an object are the interest points that best characterize the object. Finally, we calculate thresholds of each object for object recognition. User query is processed in a similar way. A given query image's local feature descriptors are extracted and then compared with the representative points of objects in the database. Especially, to reduce the number of comparisons required, we propose a method for merging descriptors of similar representative points into a single descriptor. This descriptor is different from typical SURF descriptor in that each element represents not a single value but a range. By using this merged descriptor, we can calculate the similarity between input image descriptor and multiple descriptors in database efficiently. In addition,since our scheme treats all the objects independently, it can recognize multiple objects simultaneously. (C) 2012 Elsevier Ltd. All rights reserved.
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공과대학 (전기전자공학부)
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