Background Subtraction with Dirichlet Processes

Abstract

Abstract. Background subtraction is an important first step for video analysis, where it is used to discover the objects of interest for further processing. Such an algorithm often consists of a background model and a regularisation scheme. The background model determines a perpixel measure of if a pixel belongs to the background or the foreground, whilst the regularisation brings in information from adjacent pixels. A new method is presented that uses a Dirichlet process Gaussian mixture model to estimate a per-pixel background distribution, which is followed by probabilistic regularisation. Key advantages include inferring the perpixel mode count, such that it accurately models dynamic backgrounds, and that it updates its model continuously in a principled way.

Background Subtraction on Embedded Hardware

“…A recursive method with an improved update of the Gaussian parameters and an automatic selection of the number of modes was presented in [202]. Haines et al [58] also propose an automatic mode selection method, but with a Dirichlet process. A splitting GMM that relies on a new initialization procedure and a mode splitting rule was proposed in [46,48] to avoid over-dominating modes and resolve problems due to newly static objects and moved away background objects while a multi-resolution block-based version was introduced in [146].…”

“…This includes the well-known methods by Stauffer and Grimson [156], a self-adapting GMM by KaewTraKulPong [74], the improved GMM method by Zivkovic and Heijden [202], the multiresolution block-based GMM (RECTGAUSS-Tex) by Dora et al [146], GMM method with a Dirichlet process (DPGMM) that automatically estimated the number of Gaussian modes [58] and the SGMM and SGMM-SOD methods by Evangelio et al [48,46] which rely on a new initialization procedure and novel mode splitting rule. We also included a recursive per-pixel Bayesian approach by Porikli and Tuzel [138] which shows good robustness to shadows according to [54].…”

Moving Object Detection for Visual Surveillance Using Quasi-euclidian Distance

“…The BGS technique has many review articles [2,[5][6][7][8][9][10] proposed a GMM-based approach-it uses the concept of GMM for each pixel density using pixel process. This model resolves dynamic background-and noise-related issues using multimodel GMM-based probability distribution.…”

“…[1,2]. The main aim of object detection in video is to locate and segment moving object than tracking the moving object in each frame.…”

Object Detection and Tracking in Discriminant Subspace

“…Background subtraction approach suffers from false alarms mainly because of illumination changes and dynamic background [17,21,23]. Background subtraction based moving object detection methods heavily depends on the background model.…”