relation: https://khub.utp.edu.my/scholars/11622/
title: Incremental Tensor-Based Completion Method for Detection of Stationary Foreground Objects
creator: Kajo, I.
creator: Kamel, N.
creator: Ruichek, Y.
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. In addition, the proposed method is computationally simple, and its complexity is lower compared to other approaches; hence, it can adequately satisfy real-time requirements. © 2018 IEEE.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2019
type: Article
type: PeerReviewed
identifier:   Kajo, I. and Kamel, N. and Ruichek, Y.  (2019) Incremental Tensor-Based Completion Method for Detection of Stationary Foreground Objects.  IEEE Transactions on Circuits and Systems for Video Technology, 29 (5).  pp. 1325-1338.  ISSN 10518215     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047833893&doi=10.1109%2fTCSVT.2018.2841825&partnerID=40&md5=570be77448a06a605e3d57d1f35290e7
relation: 10.1109/TCSVT.2018.2841825
identifier: 10.1109/TCSVT.2018.2841825