Incremental tensor-based completion method for detection of stationary foreground objects

In tasks such as abandoned luggage detection and stopped car detection, Stationary Foreground Objects (SFOs) need to be detected and properly classified in real time. Different methods have been proposed to detect SFOs, but they are mainly focused on certain types of objects. In this paper, an incre...

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Main Authors: Kajo, I., Kamel, N., Ruichek, Y.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.20912 /
Published: Institute of Electrical and Electronics Engineers Inc. 2018
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047833893&doi=10.1109%2fTCSVT.2018.2841825&partnerID=40&md5=570be77448a06a605e3d57d1f35290e7
http://eprints.utp.edu.my/20912/
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spelling utp-eprints.209122019-02-26T02:57:07Z Incremental tensor-based completion method for detection of stationary foreground objects Kajo, I. Kamel, N. Ruichek, Y. In tasks such as abandoned luggage detection and stopped car detection, Stationary Foreground Objects (SFOs) need to be detected and properly classified in real time. Different methods have been proposed to detect SFOs, but they are mainly focused on certain types of objects. In this paper, an incremental singular value decomposition-based method is presented to detect all types of SFOs such as abandoned objects and removed objects. The proposed method decomposes the video tensor spatiotemporally and divides it into background and foreground components. An appropriate analysis is applied to the foreground tensor to define a pixel time series of each stationary foreground category. Such analysis leads to the fact that SFOs can be detected easily owing to their continuous persistence in the decomposed foreground tensor. Furthermore, the unique structure of the pixel time series of each category allows identifying the category of the detected objects, whether they are abandoned or removed, and detecting the exact time of the start and end of each event. The results demonstrate that the proposed method achieves a superior performance in detecting SFOs at both object and pixel levels. Additionally, the proposed method is computationally simple, and its complexity is lower compared to other approaches; hence, it can adequately satisfy real-time requirements. IEEE Institute of Electrical and Electronics Engineers Inc. 2018 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047833893&doi=10.1109%2fTCSVT.2018.2841825&partnerID=40&md5=570be77448a06a605e3d57d1f35290e7 Kajo, I. and Kamel, N. and Ruichek, Y. (2018) Incremental tensor-based completion method for detection of stationary foreground objects. IEEE Transactions on Circuits and Systems for Video Technology . http://eprints.utp.edu.my/20912/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description In tasks such as abandoned luggage detection and stopped car detection, Stationary Foreground Objects (SFOs) need to be detected and properly classified in real time. Different methods have been proposed to detect SFOs, but they are mainly focused on certain types of objects. In this paper, an incremental singular value decomposition-based method is presented to detect all types of SFOs such as abandoned objects and removed objects. The proposed method decomposes the video tensor spatiotemporally and divides it into background and foreground components. An appropriate analysis is applied to the foreground tensor to define a pixel time series of each stationary foreground category. Such analysis leads to the fact that SFOs can be detected easily owing to their continuous persistence in the decomposed foreground tensor. Furthermore, the unique structure of the pixel time series of each category allows identifying the category of the detected objects, whether they are abandoned or removed, and detecting the exact time of the start and end of each event. The results demonstrate that the proposed method achieves a superior performance in detecting SFOs at both object and pixel levels. Additionally, the proposed method is computationally simple, and its complexity is lower compared to other approaches; hence, it can adequately satisfy real-time requirements. IEEE
format Article
author Kajo, I.
Kamel, N.
Ruichek, Y.
spellingShingle Kajo, I.
Kamel, N.
Ruichek, Y.
Incremental tensor-based completion method for detection of stationary foreground objects
author_sort Kajo, I.
title Incremental tensor-based completion method for detection of stationary foreground objects
title_short Incremental tensor-based completion method for detection of stationary foreground objects
title_full Incremental tensor-based completion method for detection of stationary foreground objects
title_fullStr Incremental tensor-based completion method for detection of stationary foreground objects
title_full_unstemmed Incremental tensor-based completion method for detection of stationary foreground objects
title_sort incremental tensor-based completion method for detection of stationary foreground objects
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2018
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047833893&doi=10.1109%2fTCSVT.2018.2841825&partnerID=40&md5=570be77448a06a605e3d57d1f35290e7
http://eprints.utp.edu.my/20912/
_version_ 1741196415553503232
score 11.62408

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