An eXtended Center-Symmetric Local Binary Pattern for Background Modeling and Subtraction in Videos
We propose an eXtended Center-Symmetric Local Binary Pattern (XCS-LBP) descriptor for background modeling and subtraction in videos. By combining the strengths of the original LBP and the similar CS ones, it appears to be robust to illumination changes and noise, and produces short histograms, too. The experiments conducted on both synthetic and real videos (from the Background Models Challenge) of outdoor urban scenes under various conditions show that the proposed XCS-LBP outperforms its direct competitors for the background subtraction task.
The XCS-LBP Descriptor
Figure: The XCS-LBP descriptor
The main advantage
Produces a small histogram as CS-LBP, but it extracts more image details.
Experimental Results
We’ve compared XCS-LBP with three other texture descriptors among the reviewed ones, namely :
Original LBP Ojala et al. (2002),
CS-LBP Heikkila et al. (2009) and
CS-LDP Xue et al. (2011)
We evaluate the performance with two popular background subtraction methods: Adaptive Background Learning (ABL) (also know as Running Average) and Gaussian Mixture Models (GMM). The BMC (Background Models Challenge) data set of Vacavant et al. (2012) was chosen, and it contains several synthetic and real world videos of outdoor situations (urban scenes).
Figure: Background subtraction results using the GMM method on real world videos of the BMC – (a) original frame, (b) ground truth, (c) LBP, (d) CS-LBP, (e) CS-LDP and (f) proposed XCS-LBP.
Figure: Performance of the different descriptors on real world videos of the BMC using the GMM method
Speed Comparison
XCS-LBP shows slightly better time performance than both CS-LBP and CS-LDP.
Figure: Elapsed CPU times over LBP times
Conclusion and Future Research
The experimental results show that the XCS-LBP outperforms qualitatively and quantitatively its direct competitors, making it a serious candidate for the background subtraction task in computer vision applications.
The XCS-LBP produces a shorter histogram and it is more tolerant to illumination changes and robust to noise.
Future works will explore how to extend the proposed descriptor to include temporal relationships between neighboring pixels.
Publication
2015 - Silva, Caroline; Bouwmans, Thierry; Frelicot, Carl. "An eXtended Center-Symmetric Local Binary Pattern for Background Modeling and Subtraction in Videos". The 10th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP), Berlin, Germany (oral presentation), March, 2015. [PDF] [PRESENTATION]
Python & Matlab Source Code
You can find different LBP variants codes in C++ in my GitHub repository at: https://github.com/carolinepacheco/lbplibrary
