INFORMATION PAPER International Journal of Recent Trends in Engineering, Vol. 1, No. 2, May 2009
Cluttered Background Removal in Static Images with Mild Occlusions B. Nagarajan1 and P. Balasubramanie2 1
Bannari Amman Institute of Technology, Sathyamangalam – 638 401,Tamilnadu, India E-mail:
[email protected] 2 Kongu Engineering College, Perundurai -638 052,Tamilnadu, India Email:
[email protected] information instead of intensity information. Background subtraction technique is mostly used for motion pictures to segment the foreground object by most of the researchers like McIvor [2] and Ivanov, et al. [3]. PierreMarc Jodoin, et al. [4] has proposed their work on a novel spatial framework for the background subtraction problem based on the analysis of temporal color/intensity distribution. It allows statistical motion detection with methods trained on one background frame instead of a series of frames as is usually the case. Gordon, et al. [5] describes and demonstrates a background estimation and removal method based on the joint use of range and color data clustered at each image pixel. Liyuan Li, et al. [6] has presented their work on foreground object detection through foreground and background classification under bayesian framework. Makito Seki, et al. [7] and Richord J. Radke, et al. [8] presents a novel background subtraction method for detecting foreground objects in dynamic scenes. The algorithm employs the property of image variations at neighboring image blocks have strong correlation, also known as co-occurrence. Jun-Wei Hsieh, et al. [9] proposed a novel vehicle classification scheme for estimating important traffic parameters from video sequences. The proposed system well tackles the problem of vehicle occlusions caused by shadows. Object classification system focus on the region of interest which is segmented from the background. Nagarajan and Balasubramanie have proposed their works on object classification [10-14]. The background removal plays a vital role in the preprocessing stages. Background together with mild occlusions are also removed to certain extend. The organization of the paper is as follows: Section 2 focuses on outline of the approach, Section 3 deals on experiment and results, Section 4 concludes with conclusion.
Abstract— Cluttered background removal in static images with mild occlusion remains still a challenging task for object identification or classification problems. In most of the real-world images, vehicle objects with cluttered background containing trees, road views, buildings, people, etc tent to be a noisy data or leads to the problem of clarity. The background feature covers the major portion of the image. Classification of objects fails due to cluttered background features and occluded features. This paper presents a novel approach for background removal in static images containing car object with cluttered background and mild occlusion. The morphological operations like region filling technique, background subtraction along with mapping function are used to extract the region of interest being the vehicle object. A critical evaluation of the proposed approach with the University of Illinois, UrbanaChampaign (UIUC) standard database is presented. Index Terms— Background Removal, Static Images, Mild Occlusion, Background Subtraction, Mapping Function.
I. INTRODUCTION Background removal has been a focus of investigation over last decades. Object background removal plays a major role in applications such as security systems, traffic surveillance system, target identification, etc. Most of the literature concentrates on background removal based on dynamic motion pictures. Since dynamic motion information is no longer usable for static images, background elimination becomes a more difficult task. Background removal in static images with mild occlusions remains still a challenging task especially for object identification or classification problems where the focus is on the object. In reality, these classification systems face two types of problem. (i) Objects with noisy data which leads to the problem of clarity. (ii) The objects with different viewing conditions like occlusion, complex background containing buildings, people, trees, road views, etc. Since features are extracted from the noisy data, classification or identification of objects fails. Thus this paper presents a novel approach for background removal in static images containing vehicle object with cluttered background and mild occlusion. Dongxiang Zhou, et al. [1] has presented their work on a novel background subtraction algorithm that is capable of detecting objects of interest while all pixels are in motion. This algorithm makes use of texture feature
II. OUTLINE OF THE APPROACH
In general there is no universally applicable segmentation technique that will work for all images. The overall complexity increases for the natural images as the object of interest is lying on the background region. In object classification or identification problem, it is essential to distinguish the object of interest and the background. The proposed approach is well suitable for texture based background removal with mild occlusion on the object. The background removal process is done in four stages. Capturing the grayscale image or converting a colored image into grayscale forms stage 1. Background
Corresponding Author : B. Nagarajan, Assistant Professor, Department of Computer Applications, Bannari Amman Institute of Technology, Sathyamangalam – 638 401, Tamilnadu, INDIA.
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INFORMATION PAPER International Journal of Recent Trends in Engineering, Vol. 1, No. 2, May 2009
= A∩X c
fill operation is performed in stage 2. Background subtraction is done in stage 3. In stage 4 region mapping is done to restore the original intensity values for the region of interest as that of the input grayscale image. Figure 1 describes the outline of the background removal and region of interest extraction process.
Stage 4: Mapping function (3) is used to restore the object of interest from that of the subtracted image. The resultant image is shown in Figure 5.
O(x,y)
Stage 1: Gray Scale Image
=
{
0 , if D ( x , y ) = 0 A ( x , y ), Otherwise
}
(3)
Where, O(x,y) is the transformed image, D(x,y) is image difference after fill operation and A(x,y) is the original image. Thus the object of interest is segmented from the cluttered background with mild occlusion is achieved through various stages of processing from stage 1 to stage 4.
Stage 2: Background Fill Operation
Stage 3: Image Subtraction
Stage 4: Region of Interest Mapping Figure 1. Background Removal and Region of Interest Extraction Process Figure 2. Grayscale Image (A)
Figure 3. Background Fill Operation (X)
Figure 4. Image Subtraction (D)
Figure 5. Object of Interest (O)
III. EXPERIMENT AND RESULTS This paper addresses the issues to remove cluttered background in static images with mild occlusion of realworld images containing side view of cars. The object of interest to segment is the region of car images taken from University of Illinois at Urbana-Champaign (UIUC) standard database [15]. The UIUC data set consists of 500 real uniform sizes of car images of the dimension 100X40 pixels. The experiment is done with different kinds of cars against a variety of background with partial occlusions. Car images background are found to be complex with a mixer of buildings, people, trees, road views, etc. Mild occlusions are found on car images with trees, people, vehicles, roadside objects or obstacle etc. The implementation part of the algorithm with different stages as depicted in Figure 1 is presented from Figure 2 to Figure 5. Stage 1: Original image denoted as A in grayscale is shown in Figure 2. If the input is a colored image, then it has to be converted to gray scale format for processing. Stage 2: Convolve the image with a region filling technique using the morphological operation (1) and the resultant image is shown in Figure 3.
X k = ( X k −1 ⊕ B) ∩ Ac ; X 0 = p and k = 1,2,3,.... Where, ( X
{
⊕ B) = z | ( Bˆ ) z ∩ X ≠ φ
}
IV. DISCUSSION Texture segmentation becomes important when object in a scene have a textured background. Since texture often contains high density of edges, segmentation or classification of objects is quite a difficult problem. Occlusion is static images add complexity to the existing problem. Figure 2 shows textured and cluttered background with mild occlusion as input image. Background is removed through various stages as depicted in Figure 1 and the output is shown in Figure 5. Few more samples are taken from the UIUC standard car database [15] and various results of stage 1 to stage 4 are presented in Figure 6. V. CONCLUSION Background removal in static images with mild occlusions still remains a challenging task for object identification or classification problems. An attempt has been made to remove the background and segment the object of interest. Mapping function replaces the region of interest with the original intensity values after the background removal process. Thus the features extraction is free from noisy data that represents the background region for object identification or classification problem. Experiment tested with different kinds of cars against variety of background with partial occlusions taken from UIUC standard database [15] gives satisfactory results.
(1)
Fills a region in A, given a point p in the region. B denotes the structuring element with two-dimensional four-connected neighborhood connectivity. Stage 3: Compute the absolute difference of images using (2) from Stage 1 and Stage 2, which is shown in Figure 4.
D = A − X = {ω | ω ∈ A, ω ∉ X }
(2) 135
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INFORMATION PAPER
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International Journal of Recent Trends in Engineering, Vol. 1, No. 2, May 2009
Stage 1 Gray Scale Image
Stage 2 Background Fill Operation
Stage 3 Image Subtraction
Stage 4 Region of Interest Mapping
Figure 6. Samples from UIUC standard car database with background removal and region of interest extraction process.
ACKNOWLEDGMENT The authors would like to thank the software MATLAB from Mathworks. They would also like to thank their management for the constant support towards R&D activities. REFERENCES [1]
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Dongxiang Zhou, Hong Zhang and Nilanjan Ray, “Texture Based Background Subtraction,” Proc. IEEE Int. Conf. on Information and Automation, Zhangjiajie, China, pp. 601-605, 2008. A. McIvor, “Background subtraction techniques,” in Proc. Image Video Computing, pp. 147-153, 2000. Y. Ivanov, A. Bobick, and J. Liu, “Fast lighting independent background subtraction,” Int. Journal on Comp. Vision, vol. 37, no. 2, pp. 199–207, Jun. 2000. Pierre-Marc Jodoin, Max Mignotte and Janusz Konrad, “Statistical Background Subtraction Using Spatial Cues,” IEEE Trans. Circuits and Systems for Video Technology, vol. 17, no. 12, pp. 1758-1763, Dec 2007. G. Gordon, T. Darrell, M. Harville and J. Woodfill, “Background Estimation and Removal Based on Range and Color,” Proc. IEEE Computer Society Conference on Comp. Vision and Pattern Recog.,Fort Collons, Jun 1999. Liyuan Li, Weimin Huang, Irene Yu-Hua Gu and Qi Tian, “Statistical Modeling of Complex Backgrounds for Foreground Object Detection,” IEEE Transactions on Image Proc., vol. 13, no. 11, pp. 1459-1472, Nov 2004. Makito Seki, Toshikazu Wada, Hideto Fujiwara and Kazuhiko Sumi, “Background Subtraction based on Cooccurrence of Image Variations,” Proc. IEEE CS Conf. on Comp. Vision and Pattern Recog., 2003. Richord J. Radke, Srinivas Andra, Omar Al-Kofahi and Badrinath Roysam, “Image Change Detection Algorithms :A Systematic Survey,” IEEE Trans. on. Image Proces., vol. 14, no. 3, pp. 294–307, Mar 2005. Jun-Wei Hsieh, Shih-Hao Yu, Yung-Sheng Chen and WenFong Hu, “Automatic Traffic Surveillance System for Vehicle Tracking and Classification,” IEEE Trans. on Intell. Transport. Sys., vol.7, no.2, pp.175-187, Jun 2006. B. Nagarajan and P. Balasubramanie, “Wavelet Feature based Neural Classifier System for Object Classification with Complex Background,” Proc. IEEE CS International Conf. on Computational Intelligence and Multimedia Applications, vol. 1, pp.272-279, 2007.
[11] B. Nagarajan and P. Balasubramanie, “Neural Classifier System for Object Classification with Cluttered Background Using Invariant Moment Features,” Int. Jounal of Soft Computing, vol. 3, no. 4, pp. 302-307, 2008. [12] B. Nagarajan and P. Balasubramanie, “Object Classification in Static Images with Cluttered Background using Statistical Feature based Neural Classifier,” Asian Journal of Info. Tech., vol. 7, no. 4, pp. 162-167, 2008. [13] B. Nagarajan and P. Balasubramanie, “Neural Classifier for Object Classification with Cluttered Background Using Spectral Texture Based Feature,” Journal of Artificial Intelligence, vol. 1, no. 2, pp. 61-69, 2008. [14] B. Nagarajan and P. Balasubramanie, “An Invariant Approach to Object Classification Using Background Removal,” Proc. IEEE International Conference on Computing Communication and Networking, 2008. [15] UIUC car dataset (Agarwal and Roth, 2002), http://L2r.cs.uiuc.edu/~cogcomp/Data/Car
BIOGRAPHIES B. Nagarajan received MCA degree from Madras University, India in 1997 and M.Phil. degree in Computer Science in 2002. Currently he is pursuing the Ph.D Degree. He has worked as a Co-Investigator in a research project funded by DRDO, Newdelhi, India during 2003 and 2005. He is a member of ISTE and ACEEE. He has published six papers
in International Journals. P. Balasubramanie post graduated from Bharathiar University, India in 1990. He obtained his M.Phil. Degree in Mathematics and Ph.D. Degree in Discrete Mathematics from Anna University in 1992 and 1996 respectively. He was awarded Research fellowship by Council of Scientific and Industrial Research (CSIR) in 1990. He has published more than 25 papers in National and International Journals. Presently he is working as Professor of Computer Science and Engineering, Kongu Engineering College, Tamil Nadu, India.
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