지능 영상 감시를 위한 흑백 영상 데이터에서의 효과적인 이동 투영 음영 제거An Effective Moving Cast Shadow Removal in Gray Level Video for Intelligent Visual Surveillance
- Other Titles
- An Effective Moving Cast Shadow Removal in Gray Level Video for Intelligent Visual Surveillance
- Authors
- 응웬탄빈; 정선태; 조성원
- Issue Date
- Apr-2014
- Publisher
- 한국멀티미디어학회
- Keywords
- Cast Shadow Removal; Moving Object Detection; Intelligent Visual Surveillance; Blob Segmentation
- Citation
- 멀티미디어학회논문지, v.17, no.4, pp.420 - 432
- Journal Title
- 멀티미디어학회논문지
- Volume
- 17
- Number
- 4
- Start Page
- 420
- End Page
- 432
- URI
- http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/10457
- DOI
- 10.9717/kmms.2014.17.4.420
- ISSN
- 1229-7771
- Abstract
- In detection of moving objects from video sequences, an essential process for intelligent visual surveillance, the cast shadows accompanying moving objects are different from background so that they may be easily extracted as foreground object blobs, which causes errors in localization, segmentation, tracking and classification of objects. Most of the previous research results about moving cast shadow detection and removal usually utilize color information about objects and scenes. In this paper, we proposes a novel cast shadow removal method of moving objects in gray level video data for visual surveillance application. The proposed method utilizes observations about edge patterns in the shadow region in the current frame and the corresponding region in the background scene, and applies Laplacian edge detector to the blob regions in the current frame and the corresponding regions in the background scene. Then, the product of the outcomes of application determines moving object blob pixels from the blob pixels in the foreground mask. The minimal rectangle regions containing all blob pixles classified as moving object pixels are extracted. The proposed method is simple but turns out practically very effective for Adative Gaussian Mixture Model-based object detection of intelligent visual surveillance applications, which is verified through experiments.
- Files in This Item
-
Go to Link
- Appears in
Collections - College of Information Technology > Department of Smart Systems Software > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/10457)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.