Implementation and Performance Evaluation of Background Subtraction Algorithms

Abstract

ABSTRACT

Human gait classification using combined HMM & SVM hybrid classifier

“…The algorithm can deal with dynamic challenges like shadow, flying bird, falling leaves etc. and considered to be give accurate output in most worst case scenarios [33]. A pixel process of pixel M is the values from frame 1 to frame t also represented as…”

“…The challenges involved in identification of a person by gait mainly during background removal are improper lighting condition, changes in clothing of walking subject and weather conditions (such as foggy, rainy and snowy), subject insertion or deletion (such as flying bird, falling leaves and snow fall) [33]. Alterations in gait patterns are observed due to change in mood [29] and footwear [34], patients with diabetes [35] and Parkinson's [30].…”

Human Gait Analysis and Recognition Using Support Vector Machines

“…The current frame is simply subtracted from the previous frame, and if the difference in pixel values for a given pixel is greater than that of threshold h T then the pixel is considered part of the foreground [17]. The estimated background is just the previous frame and it is very sensitive to the threshold h T .…”

Biometric Gait Recognition Based on Machine Learning Algorithms

“…The background subtraction, see (Javed et al, 2002) and (MCBS, 2013), is applied to identify a moving object against a static background by estimating pixel properties of this static background. In fact, there are different background subtraction techniques, see for instance (Das and Saharia, 2014), such as frame difference, real time background subtraction and shadow detection and adaptive background mixture model for real time tracking.…”