A temporal-spatial background modeling of dynamic ... - Springer Link

Front. Comput. Sci. China 2011, 5(3): 290–299 DOI 10.1007/s11704-011-0377-3

RESEARCH ARTICLE

A temporal-spatial background modeling of dynamic scenes Jiuyue HAO (✉)1,2, Chao LI1,2, Zhang XIONG1,2, Ejaz HUSSAIN1 1 School of Computer Science and Engineering, Beihang University, Beijing 100191, China 2 Shenzhen Key Laboratory of Data Vitalization, Research Institute in Shenzhen, Beihang University, Shenzhen 518057, China

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2011

Abstract Moving object detection in dynamic scenes is a basic task in a surveillance system for sensor data collection. In this paper, we present a powerful background subtraction algorithm called Gaussian-kernel density estimator (G-KDE) that improves the accuracy and reduces the computational load. The main innovation is that we divide the changes of background into continuous and stable changes to deal with dynamic scenes and moving objects that first merge into the background, and separately model background using both KDE model and Gaussian models. To get a temporalspatial background model, the sample selection is based on the concept of region average at the update stage. In the detection stage, neighborhood information content (NIC) is implemented which suppresses the false detection due to small and un-modeled movements in the scene. The experimental results which are generated on three separate sequences indicate that this method is well suited for precise detection of moving objects in complex scenes and it can be efficiently used in various detection systems. Keywords temporal-spatial background model, Gaussian-kernel density estimator (G-KDE), dynamic scenes, neighborhood information content (NIC), moving object detection

1

Introduction

The concept of smart cities is becoming popular throughout the world and many governments and local agencies are spending a large amount of money and

Received May 3, 2010; accepted February 6, 2011 E-mail: [email protected]

infrastructure to investigate these new born research areas for facilitating project management processes. Nowadays, a countless number of cameras, and many other intelligent surveillance devices are becoming a part of our daily lives to monitor and record any event in our lives. In all these processes or applications, moving object detection is both a fundamental and critical task. At present, most automated surveillance systems typically use stationary sensors to monitor a scene of interest. However, the static scene hypothesis does not exist in real world complex scenes, for example those containing a rotating ceiling fan, a swinging pendulum, swaying trees in the wind, water ripples, rain and so on. A common approach for identifying moving objects is background subtraction. Even though many background modeling algorithms have been proposed in the literature, the problem of identifying or extracting moving objects from a complex environment is still a far from being fully resolved. For comprehending this complex phenomenon, we can divide the process into steps. First, a chosen background subtraction algorithm should have the capability to eliminate the background interference, it should have inherent features to handle adverse weather conditions and it should be responsive so as to accommodate various levels of illumination at different times of the day. Second, changing shadows cast by a moving object should be appropriately removed from the available information. Third, the background subtraction algorithm should be robust enough to differentiate the moving objects that first merge into the background and later move into the foreground. In addition, to accommodate the realtime needs of many applications, the background subtraction algorithm must be computationally inexpensive and have low memory requirements, while still being able to accurately identify moving objects in the video.

Jiuyue HAO et al. A temporal-spatial background modeling of dynamic scenes

In this paper, we propose Gaussian kernel density estimation (G-KDE), which considers spatial correlation in a region for detecting movement of objects in a dynamic scene. Two distinctive features included in this work are as follows: 1) to address the highly dynamic property of background, a temporal-spatial model is directly established by using a G-KDE model which has the property to address both stable and continuous changes. 2) The developed moving object segment method, based on neighborhood information content (NIC), greatly suppresses the false detections due to small and un-modeled movements. Section 2 presents a detailed review of relevant works and challenges. Section 3 describes details of the proposed G-KDE temporal background modeling and spatial updating process. Section 4 covers the moving object detection model that comprises a NIC foreground detection. Experimental results and the conclusions drawn from these experiments are presented in Sections 5 and 6 respectively.

2

Related researches

Many researchers currently have their attention on object detection algorithms for complex outdoor scenes [1]. The techniques used by the research community can be divided into two categories: 1) pixel-based, 2) regionbased. Mixture of Gaussian (MoG) [2] is the most popular pixel-based method. A pixel is represented by k-Gaussian density (k is the number of the Gaussian function and must be determined beforehand) and parameters such as mean, covariance, and k, are updated or new Gaussian distributions are added at run-time. Many algorithms [3–5] have been proposed to improve MoG and its update process. Tuzel [6] et al. proposed to estimate the probability distribution of the mean and covariance of each Gaussian distribution using recursive Bayesian learning. Han [7] et al. introduced a density model which dynamically detects Gaussian components using the variable-bandwidth mean shift, and allows the number of the Gaussian function to adapt with time. Klare and Sarkar [8] improved MoG background model classifiers based on original color and Haar features to adapt to significant changes of illumination. However, when the actual density function has a large number of peaks, or a certain peak constantly changes, it is hard to describe the distribution of each pixel

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by the mixture of Gaussian model. To address some of these issues, Elgammal et al. [9] used a kernel density estimation approach, where they represent a background pixel with the individual pixels of the last N frames. This method does not require any assumptions for the model and works well for complex dynamic scenes. Based on a nonparametric density estimation method, Sheikh et al. [10] employ RGB features and location information into models and both background and foreground are modeled to augment the detection of objects. They proposed a MAP-MRF decision framework to detect the objects of interest. Mittal et al. [11] proposed a motion-based background model, which utilizes two components for the optical flow and three for the intensity in the normalized color space. However, non-parameter density estimation needs large storage memory and has a high time cost, which is hard to implement in real-time surveillance system. Considering efficiency, many methods which are not based on a density model are developed. Cutler et al. [12] use the median at each pixel location of all frames in the buffer to define the background. Haritaoglu et al. [13] developed the W4 system which represents the minimum and maximum value together with the maximum allowed change of the value in two consecutive frames. Heikkila et al. [14] represent each background pixel as a bit sequence, where each bit reflects whether the value of a neighboring pixel is above or below the pixel of interest. Yao and Odobez [15] proposed a robust multi-layer background model based on photometric invariant color measurements and texture represented by local binary patterns (LBP), and compute the distance between these measurements to update the algorithm. Although these methods have low time cost, it is difficult to determine the number of pixels in the sample list. Both [14] and [15] made the background model invariant to monotonic illumination changes. In [16], Cheung compared various background subtraction algorithms including Median Filter [12], Approximated Median Filter [17], Kalman Filter [18], and Mixture of Gaussians (MoG) [2] in urban traffic video sequences. The experimental results in [16] demonstrate MoG achieves the best precision and recall, MF is very close second, followed by AMF and KF. A common limitation of the pixel-based techniques described above is that they ignore the dependencies of neighboring pixels. To overcome this limitation a new category of methods has been introduced which uses region-based background modeling. Oliver et al. [19]

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applied an eigenspace representation on the whole frame and detected new objects by comparing the input image with an image reconstructed via the eigenspace. Eng et al. [20] divided a background model learned over time into a number of non-overlapping blocks and grouped pixels within each block into several classes. To combine the advantages of both methods, many temporal-spatial background models have been proposed. Cristani [21] proposed a Spatial-Time adaptive per pixel mixture of Gaussian (S-TAPPMOG) which considers the neighborhood zone throughout a sampling process. Barnich [22] uses a random policy to select values to build a samplesbased estimation of background called VIBE.

3

Temporal-spatial background modeling

The traditional pixel-based model includes only temporal features and does not involve the dependencies of neighboring pixels. However, a pixel value in a dynamic scene may have periodic movement within a fixed region. Thus, we first use median filtering for pre-processing, which can smooth the interference caused by significant variation of pixel values, and reduce the number of density function peaks. For example, Fig. 1(a) shows a complex outdoor surveillance scene from where we collected gray values of a pixel, at location A, over a length of 900 frames. The information is then depicted graphically as shown in Fig. 1(b), where we can calculate the average rate of change between adjacent frames is 10.38%. We get a reduced value of about 4.53% after pre-processing the variation of gray values as depicted in Fig. 1(c). Furthermore, as shown in Fig. 2(a), the original intensity distribution stretches to a very wide range of gray levels, but by using a median filter the distribution becomes more concentrated as shown in Fig. 2(b). However, both of the intensity histograms include many peaks and dramatically changing behavior over very short periods of time. The model with a small number of Gaussian distributions cannot describe the background accurately. Thus, we employ a kernel density estimation method, which does not need the assumption of background distribution and captures recent information about the image sequence, and hence propose as a new background modeling scheme. 3.1

G-KDE background modeling

In this section, we propose a new background modeling scheme based on two concepts, continuous change and

Fig. 1 (a) Outdoor scene, where point “A” shows the location of the sample pixel; (b) gray values of original sequence; (c) gray values after pre-processing

stable change. Continuous change is rapid change at one pixel in a short period of time caused by dynamic background. (e.g., swing of trees in the wind). Stable change can be caused by either the introduction or removal of objects or continual illumination changes over a long time. (e.g., a car appears, and stops in the scene). Traditional methods, e.g., MoG, did not mark the boundary between the two concepts and used uniform modeling methods.

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bandwidth. Then, the density can be defined as Eq. (2) K¼

1 1 d ð2pÞ 2 jΛj 2

e – 2 ðxt – xi Þ 1

T

Λ – 1 ðxt – xi Þ

,

(2)

where d is the dimension of feature vector. We only use RGB color space, and then d is equal to 3. If we assume independence between different color channels with a different kernel bandwidths j for the jth color channel, then the density estimation is reduced to 3

1 – 12 K¼ e 3 Π ð2pÞ 2 j¼1 j 1

3.2

Fig. 2 Intensity histograms of pixels over time at point A. (a) Original sequences; (b) after pre-processing

In this paper, we design a new model. To address continuous and stable changes, we use KDE to deal with continuous changes, and single Gaussian to update stable changes. Since continuous changes cannot be described by simple multi-mixture functions, KDE can obtain the accuracy probability through training samples. Many stable changes in each pixel may be generated by a single object, such as a bag, a car, etc., thus the probability can be described simply by a single Gaussian function. Let XN ¼ fx1 ,x2 ,:::,xi ,:::,xn g be a sample of intensity values for a pixel during a period of time. This pixel has intensity value xt at time t. The probability density function is expressed as pðxt Þ ¼ βN – 1

n X

Kðxt – xi Þ þ ð1 – βÞGðxt Þ,

(1)

i¼1

where K is the kernel estimator function, G is the single Gaussian function, and β ð0£β£1Þ is the mixture weight, also mean  and covariance δ of G are initialized by the first N frames. In this paper, we define K to be a normal Gaussian function N ð0,ΛÞ. Λ represents the kernel function

ðxt – xi Þ2 2j

:

(3)

Updating the background model

A fast background update scheme is important to respond to abrupt changes in outdoor surveillance scenes. Traditional methods have always used a uniform update rate. Thus, it is hard to decide how fast the background model can be updated. In [6–8,20,21], they use parameters like weight, mean, and variance to achieve adaptability. In [9], a recent history sample is stored and used to continuously update in a first-in first-out manner. This method has two versions of the model. The first of these is described as a short-term model, where adding a new sample to the model depends only on if it is classified as a background sample (selective-update). The second one is a long-term model, which adds the new sample to the model without any condition and is referred to as a blind-update. The detection result computes the intersection of the two model results. However, it needs an enormous amount of memory and has a high time cost. In addition, the shortcoming of the selective update mechanism is that incorrect detection errors accumulate as it proceeds further. The blind update mechanism leads to foreground pixels which are classified as background pixels, and adds false negatives into the background, and the movement of objects may then erroneously become part of the background model. In our update stage, spatial features are involved in the sample selection stage. In dynamic a scene, pixel value x dramatically changes over time. Considering spatial relationship between pixels, we calculate the conformity of neighboring pixels with the current background mode, and recode the update sample of pixel x using region average (RA) as Eq. (4). Each pixel is represented by x.

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X x¼

px ðxj Þxj

xj 2RðLÞ

X

px ðxj Þ

,

(4)

neighborhood region of pixel xt , where W is the width of the region. When any pixel, y, is included in RðW Þ, we can define the information gap between xt and y as

xj 2RðLÞ

where px is the probability density at location of pixel x which is the center pixel of the region, L is the width of window, RðLÞ is the region fixed by the window, and xj represents a pixel value located in RðLÞ. Based on the sample selection strategy, the KDE model uses a blind-update mechanism which adds each pixel as the sample frame without condition judgment. Eq.(5) calculates the new parameters of Gaussian, where gð0£ g£1Þ is the mixture weight. tþ1 ¼ t þ gðxtþ1 – t Þ,   δtþ1 ¼ δt þ g ðxtþ1 – t Þ2 – δt :

(5)

In our paper, we define KDE and Gaussian updating rates as k and g respectively. The long-term update rate for continuous changes can reduce the probability of foreground pixels contained in the background sample. On the other hand, a Gaussian model with a rapidly changing update rate ensures a correct real-time background model.

4

Moving object detection

dðxt ,yÞ ¼ IðyÞ – Iðxt Þ ¼ log

(6)

Here pxt and py are the probability densities of pixel xt and pixel y, respectively. Taking the information from Eq. (6), we can adjust the self-information of xt as m X Ixít ¼ Ixt þ ε dðxt ,yj Þ,

(7)

j¼1

where ε is the adjusted parameter. This method considers the impact of neighborhood pixels on the central pixel. If a pixel, x, is determined as foreground, but most of its neighborhood pixels, y, are a part of the background. We get pðxÞ£pðyÞ, dðx,yÞ£0, with Ixít £Ixt , which means that pixel x tends to be judged as background, and suppresses the noise in the background. On the contrary, if x is determined as the background and y as the foreground, we get pðxÞ³pðyÞ, dðx,yÞ³0 with Ixít ³Ixt . Here, pixel x is judged to be foreground, and eliminates holes in the moving object. Based on Eq. (7), we can describe a moving object by ( í Ixt ³Ith ,  f oreground, (8) I í < I ,   background: xt

In outdoor environments with dynamic backgrounds, false-positive detection of a moving object can occur due to random noise and background movements which are not represented by the model. Although by using G-KDE modeling, our method can efficiently reduce the effect of random noises, but still it is not capable of addressing all changes in the background. The original method sets a probability threshold to decide the classification of a pixel. In [9], the maximum probability of change in the neighborhood of a pixel and the product of neighborhood probability are computed, and two thresholds set for foreground detection. In [10], a MAP-MRF framework presents spatial context as a condition for detecting interesting objects but this method has a high time cost. In this section, we propose a neighborhood information content (NIC) framework to suppress false detections due to small and un-modeled movements in the outdoor scene. According to information theory, we define the selfinformation, I, of a pixel: I ¼ – log pðxÞ. Let RðW Þ be the

pxt ðxt Þ : py ðyÞ

5

th

Experiment results

In this section we describe a set of experiments. All algorithms performed in Matlab 7.0 on a Windows XP platform and tested on a machine with a 2GHz AMD 3200 + with 1G of RAM. A variety of video sequences of outdoor dynamic scenes have been taken with a COTS camera (Panasonic NV-GS300). There are 240  360 pixels per frame with frame rate of 25 fps (frame per second). First of all, we discuss the parameter selection procedure involved in our method. In [9], the kernel bandwidth δ is computed as δ¼

med pffiffiffi , 0:68 2

(9)

Jiuyue HAO et al. A temporal-spatial background modeling of dynamic scenes

where med is the median of jxtþ1 – xt j for each consecutive pair ðxi , xiþ1 Þ, and is computed independently for each color channel. To avoid anticipated errors, we improve the estimation method as   δ# ¼ min 10,maxð5,δÞ :

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algorithm. The sequence involved the tree branches swaying in response of strong wind. The first column shows the original frames. 2nd, 3rd and 4th columns show the results of detected foreground using MoG, KDE [10], and our proposed approach, respectively. These figures clearly show that our method significantly reduces the noise when compared with the other two approaches. Fig. 4 shows the results of a particularly challenging outdoor sequence, with four sources of dynamic motion: 1) the pool rippling in the wind; 2) the swaying of tree branches; 3) the shadows of the branches on the pool; and 4) the occlusion of subjects by a bridge. In Fig. 4, the 1st column has the original image, 2nd, 3rd, and 4th columns show the results obtained by MoG, KDE [10], and our proposed G-KDE method, respectively. MoG detected the foreground but left most of the noise. The method in [10] suppressed most of noise, but at the same time it reduced foreground pixels as well. Our proposed approach disregarded each of the dynamic phenomena and instead detected the object of interest. Furthermore, quantitative experimentations have been performed. We manually segmented a 733-frame sequence (as seen in Fig. 3) into foreground and background regions. We compared execution times of all three methods, and the results are depicted in Fig. 5. Based on non-parameter estimation methods, MoG has the lowest time cost compared to the other two. Time cost

(10)

In the update process, each frame for background modeling is updated using a single Gaussian function, thus g has the value 25fps. The detail update process of KDE is such that we append all pixels of the image to the background samples queue, and then choose the most recent 200 frames for the new KDE model after every 10000 frames. The parameters used in our algorithm are given in Table 1. After parameter selection, we compared the detection performance of our proposed background model with MoG and KED [10] models. To prove our model, we used RGB color space to check the performance of three models without NIC algorithm, and performed on a fair ground. The threshold for the foreground detection using only the probability pðxt Þ was chosen as 10–7. For MoG, a three-component mixture was modeled with a learning rate of 0.01. All three methods used 200 frames at the training stage, and shadow removal or post-processes were not used in the presentation of these results. Figure 3 shows the results obtained by our proposed Table 1 Algorithm parameters Variable

β

g

L

w

ε

Ith

Value

0.8

0.05

1

2

0.05

26.5

Fig. 3 Tree branches swaying in the wind

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Fig. 4 Poolside sequence with swaying of tree, shadow, and ripples

Fig. 5 Execution time comparison using MoG, KDE and GKDE.

for [10] based on KDE is the worst because it uses the five-dimension feature of pixels for both foreground and background modeling. Due to the heavy time cost of [10], this method is hard to implement for real-time surveillance. Our proposed update method which uses three color channels like MoG, by including spatial features during update sample selection step, ranks in the middle. Precision and recall rates for all the methods have been compared. Results of this comparison are shown in Fig. 6. Fig. 6(a) illustrates that the precision of G-KDE model is consistently higher than the mixture of Gaussian and KDE model in [10]. Especially after 230 frames when the wind becomes strong, the precision of MoG is greatly reduced. In Fig. 6(b), average recall of G-KDE model is higher than MoG as well as KDE. Overall results are tabulated in Table 2, which shows that proposed G-KDE model has an average precision of 93.21% and an average recall of 82.87%, both these

Fig. 6 Detection rate comparison of MoG, KDE and G-KDE. (a) Precision; (b) Recall

values are higher than those of the other two methods. Our G-KDE model can efficiently suppress dynamic scene noise and accurately detect the foreground. To verify our foreground detection method, we used three models with the NIC method in the original video sequence 2 (as seen in Fig. 4) for detecting a moving object. The results are shown as Figs. 7 and 8. The first row was the results obtained by original three models.

Jiuyue HAO et al. A temporal-spatial background modeling of dynamic scenes

Table 2 Average precision and recall MoG

KDE

G-KDE

Precision

79.18%

87.78%

93.21%

Recall

74.06%

81.00%

82.87%

and 8(d) show that our method cannot deal with too much noise. Thus, the NIC algorithm also needed based on a great background modeling.

6 And the 2nd row was the results obtained by adding NIC method. Figs. 7 and 8 show that our proposed method with NIC eliminates most of the noise in the background and connects the moving object components to fill holes (as seen Figs. 7(e), 7(f), 8(e) and 8(f)). However, Figs. 7(d)

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Conclusions

There are number of innovations presented in this paper. A robust, Gaussian-kernel density estimation (G-KDE) model and foreground detection mechanism are introduced. The model has temporal and spatial features which

Fig. 7 Comparison of three models with NIC method. (a) GMM; (b) KDE; (c) G-KDE; (d) GMM with NIC; (e) KDE with NIC; (f) GKDE with NIC

Fig. 8 Comparison of three models with NIC method. (a) GMM; (b) KDE; (c) G-KDE; (d) GMM with NIC; (e) KDE with NIC; (f) GKDE with NIC

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has the property to handle the situations of rapidly dynamic background of the scene. We divide all changes into continuous and stable changes, which are respectively dealt with KDE and single Gaussian models. To improve the efficiency, the update rate adapts according to the different type of changes. A foreground detection using neighborhood information content (NIC) is implemented which suppresses the false detections due to small and unmodeled movements in the scene background. The experimental results validate that the G-KDE model is well suited for precise detection of moving objects in complex and dynamic outdoor scenes and can be used effectively and efficiently in various real time surveillance systems.

of Workshop on Computer Vision and Pattern Recognition. 2009, 66–73 9. Elgammal A, Harwood D, Davis L. Non-parametric model for background subtraction. In: Proceedings of the 6th European Conference on Computer Version. 2000, 751–767 10. Sheikh Y, Shah M. Bayesian modeling of dynamic scenes for object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(11): 1778–1792 11. Mittal A, Paragios N. Motion-based background subtraction using adaptive kernel density estimation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2004, 302–309 12. Cutler R, Davis L. View-based detection and analysis of periodic motion. In: Proceedings 14th International Conference on Pattern Recognition. 1998, 495–500

Acknowledgements This work was supported by the State Key Laboratory of Software Development Environment, Beihang University (SKLSDE-2010ZX-04). It was also supported by the National High Technology Research and Development Program of China (No. 2007AA010403).

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Hao Jiuyue was born in 1984. She received the bachelor degree in computer science from Communication University of China, Beijing in 2006. She is currently pursuing her PhD degree in computer science and technology at Beihang University, Beijing, and visiting University of California, Berkeley for one year. Her research interests include computer vision, pervasive computing and intelligent transportation systems.

Xiong Zhang received his Bachelor degree from Harbin Engineering University, Heilongjiang Province, China in 1982, received the his MSc degree from Beihang University, Beijing in 1985. He is a professor and PhD supervisor in the School of Computer Science and Engineering, Beihang University. He is working on computer vision, wireless sensor networks and information security.

Li Chao received his BSc and PhD degrees in computer science and technology from Beihang University, Beijing, China in 1996 and 2005. Now he is associate professor and master supervisor in the School of Computer Science and Engineering, Beihang University. Currently, he is working on data vitalization and computer vision.

Ejaz Hussain received his Bachelor degree from UET Lahore, Pakistan in 1998 and Master degree from UET Taxila, Pakistan in 2006. Currently, he is pursuing his PhD degree in computer science and engineering in Beihang University, Beijing. His research interest includes Ad hoc sensor networks, pervasive computing and adaptive vision.