Open AccessBackground Subtraction (BS) Using Instant Pixel Histogram
Author(s) -
Narjis Mezaal Shati,
Sundos Abdulameer Alazawi,
Huda Abdulaali Abdulbaqi
Publication year - 2019
Publication title -
xi'nan jiaotong daxue xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.308
H-Index - 21
ISSN - 0258-2724
DOI - 10.35741/issn.0258-2724.54.5.14
Subject(s) - computer science , histogram , artificial intelligence , instant , background subtraction , pixel , constant false alarm rate , computer vision , subtraction , false alarm , object detection , foreground detection , anomaly detection , histogram of oriented gradients , pattern recognition (psychology) , image (mathematics) , mathematics , physics , arithmetic , quantum mechanics
Video computer vision applications require moving objects detection as a first phase of their operation. Therefore, background subtraction (BS), an investigate branch in computer vision with intensive published research, is applied to obtain the “background” and the “foreground.” Our study proposes a new BS model that utilizes instant pixel histogram, which is implemented to extract foreground objects from two datasets, the first Visor (different human actions) and the second Anomaly Detection Dataset UCSD (Peds2). The model when using the Visor dataset gives 100% detection rate with 8% false alarm rate, whereas, when using UCSD (Peds2), it achieves a detection rate and false alarm rate of 77% and 34% respectively.