Abstract
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.
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