Detecting Moving Shadows: Algorithms and Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection and Location of People in Video Images Using Adaptive Fusion of Color and Edge Information
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Foreground object detection from videos containing complex background
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Bayesian Modeling of Dynamic Scenes for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Texture-Based Method for Modeling the Background and Detecting Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental Tensor Subspace Learning and Its Applications to Foreground Segmentation and Tracking
International Journal of Computer Vision
Adaptive εLBP for background subtraction
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
ViBe: A Universal Background Subtraction Algorithm for Video Sequences
IEEE Transactions on Image Processing
Background modeling using adaptive pixelwise kernel variances in a hybrid feature space
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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Background subtraction is the first step in many video surveillance systems, its performance has a decisive influence on the result of the post-processing. An effective background subtraction algorithm should distinguish foreground from the background sensitively, and adapt to the variation of background scenes robustly, such as illumination changes or dynamic scenes. In this paper, a novel pixel-wise background subtraction algorithm is introduced. First, we propose a novel texture descriptor named Multi-Channel Scale Invariant Local Ternary Pattern(MC-SILTP). The pattern is cross-calculated in RGB color channels with the Scale Invariant Local Ternary Pattern operator. This descriptor does not only show an excellent performance in abundant texture regions, but also in flat regions. Secondly, we model each background pixel with a codebook rather than estimating the probability density functions. The codebook is consisted of many MC-SILTP samples actually observed in the past. A lot of experiments have been done over the proposed approach, results indicates that this approach is well balanced in sensitivity and robustness. It can handle the tricky problem of illumination changes robustly while detecting complete objects in flat areas sensitively. Comparison between the proposed one and several popular background subtraction algorithms demonstrates that it outperforms the state-of-the-art.