This paper presents a novel background modeling system that uses a spatial grid of Support Vector Machines classifiers for segmenting moving objects, which is a key step in many video-based consumer applications. The system is able to adapt to a large range of dynamic background situations since no parametric model or statistical distribution are assumed. This is achieved by using a different classifier per image region that learns the specific appearance of that scene region and its variations (illumination changes, dynamic backgrounds, etc.). The proposed system has been tested with a recent public database, outperforming other state-of-the-art algorithms

Blanco Adán, Carlos Roberto del

Cuevas Rodríguez, Carlos

García Santos, Narciso

Jaureguizar Núñez, Fernando

Zhang, Xiaohan

Xiaohan Zhang

Carlos R. del Blanco

Carlos Cuevas

Fernando Jaureguizar

Narciso Garcia

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High-quality region-based foreground segmentation using a spatial grid of SVM classifiers

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High-quality Region-based Foreground Segmentation 
Using a Spatial Grid of SVM Classifiers 
Xiaohan Zhang , Carlos R. del Blanco , Carlos Cuevas , Fernando Jaureguizar , and Narciso García 
Abstract—This paper presents a novel background modeling 
system that uses a spatial grid of Support Vector Machines 
classifiers for segmenting moving objects, which is a key step in 
many video-based consumer applications. The system is able to 
adapt to a large range of dynamic background situations since no 
parametric model or statistical distribution are assumed. This is 
achieved by using a different classifier per image region that 
learns the specific appearance of that scene region and its 
variations (illumination changes, dynamic backgrounds, etc.). 
The proposed system has been tested with a recent public 
database, outperforming other state-of-the-art algorithms. 
I. INTRODUCTION 
The number of consumer applications to analyze and 
extract valuable information from video sequences has rapidly 
increased along the recent years [1]. This fact is due to the 
explosion of electronic devices endowed with cameras. In 
many of these applications, foreground segmentation is a 
fundamental step that must be carried out to satisfactorily 
perform other high level tasks, such as object tracking and 
counting [2], classification, or augmented reality [3]. 
Several foreground segmentation approaches have been 
recently proposed [4]. On the one hand, some strategies focus 
on the usability and real time processing. However, they only 
obtain satisfactory results in low-complex sequences with no 
significant changes, failing in more challenging scenes 
characterized by dynamic backgrounds, illuminations changes, 
or shadows [5]. On the other hand, high-quality multimodal 
methods have been also proposed to improve the quality of the 
results in such complex scenarios [6]. These methods use 
kernel density estimation techniques to model the background 
distribution at pixelwise level. However, they have serious 
problems to scale up the background distribution using a 
region-based approach due to the famous "curse of 
dimensionality". This fact avoids to use more complex 
distributions that could improve the final segmentation. 
We propose a novel region-based spatial grid Support 
Vector Machines (SVM) classification system for high-quality 
foreground segmentation. Instead of modeling the background 
distribution at region level that has obvious problems due to 
the high dimensionality, a classification strategy is adopted, 
which is able to separate background and foreground classes 
in high dimensional spaces. An independent SVM classifier is 
used per image region to predict the belonging to the 
background and foreground classes, using Histograms of 
Oriented Gradients (HOG) [7] and color histograms as inputs. 
Promising results have been obtained in a recent public 
dataset, outperforming other state-of-the-art strategies. 
itk SVM Classifier Sequence of images 
0 HOG and color 
jth SVM Classifier 
Fig. 1 Spatial grid of SVM classifiers. 
II. SYSTEM DESCRIPTION 
The background/foreground segmentation is accomplished 
via a novel classification-based framework. A spatial grid of 
SVM classifiers is used to segment every region of an image 
into background and foreground. Every classifier is applied 
over a specific image area with the purpose that it can learn 
the specific appearance of the underlying scene region and its 
variations. The main advantage is that no assumption is made 
about the expected distribution of the background/foreground, 
which allows to deal with multimodal backgrounds and strong 
changes in the scene appearance. Besides, unlike the typical 
approach that uses a unique classifier and a sliding window, 
the proposed grid of classifiers can learn the particular 
background appearance of every image region. 
The spatial grid of SVM classifiers is structured as 
follows (see Fig. 1). Each acquired video frame is divided into 
blocks that are partially overlapped. For every block, there is a 
SVM classifier that learns and predicts the 
foreground/background classes of the underlying scene region. 
Since the straight output of the grid of classifiers is a 
downsampled version of the original image, a bilinear 
interpolation technique is used to obtain the final binary image, 
which represents the foreground/background segmentation. 
The used interpolation is the nearest neighbor. 
The details about the used image features and the training 
procedure for the SVM classifiers are given as follows. 
III. FEATURES AND TRAINING PROCEDURE 
Color and gradient information is used to characterize 
Fig. 2 (a) Original image, (b) Ground truth, (c) Confidence map, (d) Result. 
the background/foreground. Color information is encoded by 
means of color histograms, and gradient information through 
HOG descriptors. Both region-based image descriptors are 
then combined and normalized to form the input feature 
vectors of the SVM classifiers. 
Training samples for the background class are specific for 
every classifier, since they have to learn the appearance of 
different scene areas. This means that every classifier is 
trained with a different set of training features, which are 
extracted for corresponding image blocks. However, the same 
training samples are used for all the classifiers to train the 
foreground class. The reason is that the moving objects in the 
foreground could be located in any scene position. This 
training scheme produces much more foreground training 
samples than background ones. To balance both training 
datasets, the k-means clustering algorithm is used to reduce 
the number of foreground training examples. 
IV. RESULTS 
The proposed foreground segmentation algorithm has been 
tested with a public and recent dataset called Background 
Models Challenge. This dataset contains 29 sequences divided 
into 10 sequences for learning and 19 for testing. In addition, 
the following state-of-the-art background subtraction methods 
have been used for comparison purposes: Takavoli [8], 
Guyon [9], and Yoshinaga [10]. 
Figure 2 shows an example of the obtained results with the 
proposed algorithm. Fig. 2(a) shows the original image, Fig. 
2(b) the ground truth, Fig. 2(c) the confidence map from the 
grid of classifiers (values greater than zero are foreground and 
otherwise background), and Fig. 2(d) the resulting foreground 
/background binary image segmentation. Note that the 
foreground is compact and accurate, in spite of the occluding 
railing and the illumination changes. 
Table I shows the quantitative results for different 
sequences using the well-known F-measure. Observe that the 
proposed algorithm (called Grid-SVM) outperforms the other 
state-of-the-art methods. Specifically, it achieves the best 
F-measure score in all the sequences with the exception of one 
case, where it has the second best score. 
TABLE I 
FOREGROUND SEGMENTATION RESULTS USING THE F-MEASURE 
Grid-SVM 
0.887 
0.878 
0.870 
0.859 
0.874 
0.834 
0.899 
Takavoli 
0.713 
0.689 
0.699 
0.692 
0.695 
0.679 
0.695 
Guyon 
0.867 
0.853 
0.867 
0.858 
0.861 
0.711 
0.812 
Yoshinaga 
0.870 
0.875 
0.851 
0.732 
0.904 
0.822 
0.896 
V. CONCLUSION 
A novel background subtraction system for video-based 
consumer applications is proposed. The main contribution is 
the use of a spatial grid of SVM classifiers that allows the 
system to adapt to a large range of dynamic background 
situations. No parametric model or statistical distribution are 
assumed, unlike other state-of-the-art approaches. Instead, a 
different classifier is used and adapted for every image region, 
which learns the appearance of a specific scene region and its 
variations. Promising results have been obtained with a public 
database, outperforming other state-of-the-art algorithms. 
REFERENCES 
[1] C. Cuevas, D. Berjón, F. Moran, and N. García, "GPGPU 
Implementation of an Improved Nonparametric Background Modeling 
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Electronics, pp. 23-24, 2013. 
[2] C.R. del Blanco, F. Jaureguizar, and N. Garcia, "An efficient multiple 
object detection and tracking framework for automatic counting and 
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[3] E. Komagal, A. Vinodhini, A. Srinivasan, and B. Ekava, "Real time 
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[7] N. Dalai and B. Triggs, "Histograms of oriented gradients for human 
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High-quality region-based foreground segmentation using a spatial grid of SVM classifiers

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