Texture BGS of Heikkila et al. (2006) paper link
This paper presents a novel and efficient texture-based method for modeling the background and detecting moving objects from a video sequence. Each pixel is modeled as a group of adaptive local binary pattern histograms that are calculated over a circular region around the pixel. The approach provides us with many advantages compared to the state-of-the-art. Experimental results clearly justify our model.
Multi-Layer BGS of Jian Yao and Jean-Marc Odobez (2007) paper link
In this paper, we propose a robust multi-layer background subtraction technique which takes advantages of local texture features represented by local binary patterns (LBP) and photometric invariant color measurements in RGB color space. LBP can work robustly with respective to light variation on rich texture regions but not so efficiently on uniform regions. In the latter case, color information should overcome LBP’s limitation. Due to the illumination invariance of both the LBP feature and the selected color feature, the method is able to handle local illumination changes such as cast shadows from moving objects. Due to the use of a simple layer-based strategy, the approach can model moving background pixels with quasi-periodic flickering as well as background scenes which may vary over time due to the addition and removal of long-time stationary objects. Finally, the use of a cross-bilateral filter allows to implicitely smooth detection results over regions of similar intensity and preserve object boundaries. Numerical and qualitative experimental results on both simulated and real data demonstrate the robustness of the proposed method
These algorithms are contained in the bgslibrary by Andrews Sobral, that includes over 30 background subtraction algorithms, a common C++ framework for comparing them, and an handy C++/MFC or Java app to see them running on video files or live feed from a webcam.
Return to the list of background subtraction algorithms