TY  - JOUR
N2  - 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.
IS  - 5
ID  - scholars11622
KW  - Feature extraction; Interactive computer systems; Object recognition; Pixels; Real time systems; Singular value decomposition; Tensile stress; Tensors; Time series; Time series analysis
KW  -  Abandoned objects; Incremental singular value decompositions; Matrix decomposition; Spatiotemporal phenomena; Stationary foreground; Tensor completion
KW  -  Object detection
A1  - Kajo, I.
A1  - Kamel, N.
A1  - Ruichek, Y.
JF  - IEEE Transactions on Circuits and Systems for Video Technology
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047833893&doi=10.1109%2fTCSVT.2018.2841825&partnerID=40&md5=570be77448a06a605e3d57d1f35290e7
VL  - 29
Y1  - 2019///
N1  - cited By 13
SP  - 1325
TI  - Incremental Tensor-Based Completion Method for Detection of Stationary Foreground Objects
AV  - none
EP  - 1338
PB  - Institute of Electrical and Electronics Engineers Inc.
SN  - 10518215
ER  -