Robust Background Subtraction via the Local Similarity Statistical Descriptor

Abstract

Abstract:Background subtraction based on change detection is the first step in many computer vision systems. Many background subtraction methods have been proposed to detect foreground objects through background modeling. However, most of these methods are pixel-based, which only use pixel-by-pixel comparisons, and a few others are spatial-based, which take the neighborhood of each analyzed pixel into consideration. In this paper, inspired by a illumination-invariant feature based on locality-sensitive histograms proposed for object tracking, we first develop a novel texture descriptor named the Local Similarity Statistical Descriptor (LSSD), which calculates the similarity between the current pixel and its neighbors. The LSSD descriptor shows good performance in illumination variation and dynamic background scenes. Then, we model each background pixel representation with a combination of color features and LSSD features. These features are then embedded in a low-cost and highly efficient background modeling framework. The color and texture features have their own merits and demerits; they can compensate each other, resulting in better performance. Both quantitative and qualitative evaluations carried out on the change detection dataset are provided to demonstrate the effectiveness of our method.

Background Subtraction Using an Adaptive Local Median Texture Feature in Illumination Changes Urban Traffic Scenes

“…That is, all the compared models were reconstructed with a sample consensus scheme without pattern kernel density estimation or histogram approach. The ALMT feature of our model was replaced by LBP [22], LTP [26], SILTP [27], LBSP [30], LLSD [40], IIF [20] and ALTF [31] features to build corresponding LBP model (LBPM) , LTP model (LTPM), SILTP model (SILTPM), LBSP model (LBSPM), LLSD model(LLSDM), IIF model (IIFM) and ALTF model (ALTFM), respectively. Moreover, all the parameters of features in the compared methods were set to the optimum values according to the original authors' recommendations and the other parameters of consensus scheme method were set to the same value as in our model.…”

Color–Texture Pattern Classification Using Global–Local Feature Extraction, an SVM Classifier, with Bagging Ensemble Post-Processing

“…Feature extraction and selection enable representation of the information present in the image, and limit the number of features, thus allowing further analysis within a reasonable time. Feature extraction was used in a wide range of applications, such as biometrics [12,14,15], classification of cloth, surfaces, landscapes, wood, and rock minerals [16,17], saliency detection [18], and background subtraction [19], among others. During the past 40 years, while a substantial number of methods for grayscale texture classification were developed [3,5], there was also a growing interest in colored textures [1,2,9,10,13,20,21].…”