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1 Proceedings of the 013 International Conference on Pattern Recognition, Informatics and Mobile Engineering, February 1- Background Subtraction Based on Threshold detection using Modified K-Means Algorithm A. Niranjil Kumar Department of ECE P.S.R.Regnasamy College of Engineering for Women Sivakasi, Tamilnadu, India Abstract In video surveillance systems, background subtraction is the first processing stage and it is used to determine the objects in a particular scene. It is a general term for a process which aims to separate foreground objects from a relatively stationary background. It should be processed in real time. It is obtained in human detection system by computing the variation, pixel-bypixel, between the current frame and the image of the background, followed by an automatic threshold. This paper proposed a K means based background subtraction for real time video processing in video surveillance. We have analyzed and evaluate the performance of the proposed method, with standard K-means and other background subtractions algorithms. The experimental results showed that the proposed method provides better output. Keywords Background Subtraction, Foreground Detection, K means, Video Surveillance, Threshold Detection. I. INTRODUCTION In computer vision applications like multimedia, video surveillance and traffic monitoring systems background subtraction (BGS) is used to detect the moving objects in a scene. It is used to identify foreground objects in a video stream in real time. Background subtraction consists of steps, like background modeling, initialization and maintenance and foreground detection In a video surveillance system object detection can be achieved by building a background model. Any important change in an image region from the background model signifies a moving object.the most common paradigm for performing BGS is to build an explicit model of the background. Foreground objects are then detected by calculating the difference between the current frame and this background model. A binary foreground mask will be made by classifying the pixel with association of absolute distinction higher than a threshold as being from a foreground object. The basic plan is to classify pixel as background or foreground by finding the distinction between the background image and also the current image. According to importance of real-time computations in the surveillance systems, a subtraction method is so significant. In recent years wide range of BGS algorithms have been developed to improve their performance. The motion-based perceptual grouping a spatio-temporal saliency algorithm is proposed to perform BGS and it is applicable to scenes with Dr. C. Sureshkumar J. K. K. Nattraja College of Engineering and Technology Namakkal, Tamil Nadu, India ck_sureshkumar@yahoo.co.in highly dynamic backgrounds. [1] Comparison of the algorithm with other techniques shows that the cost of a prohibitive processing time is long. It takes several seconds to processes a single frame. That s why this algorithm is unsuitable for realtime applications. An on-line K-means approach is proposed by Stauffer and Grimson. In this approach, each incoming pixel is matched to a cluster if it is within.5 standard deviations from the cluster mean. The parameters for that cluster are then updated with the new observation. Repeated similar pixels will drive the weight of their cluster up and simultaneously reduce the cluster variance, indicating a higher selectivity. If no match is found, the observation becomes a new cluster with an initially low weight and larger variance. [][3] A robust vision-based system for vehicle tracking and classification is proposed and tested with different kinds of weather conditions including rainy and sunny days by Luis Unzueta [4]. The proposed system of Piccardi [5] performs in real time even in challenging situations.a background subtraction technique must adapt to illumination changes, motion changes, high frequency background objects and any sudden changes in the background of the objects. In background subtraction there are many challenges like illumination, shadows of moving objects, non-stationary background and etc. Here we propose modified K-means algorithm for background subtraction and we got better result under fast luminance changes. This paper is organized as follows: Section II overview of Background subtraction is illustrated. Proposed method is presented in Section III. Experimental results are described in Section IV. Section V conclusion of this paper is presented. II. OVERVIEW OF BACKGROUND SUBTRACTION Foreground mask for every frame is generated by background subtraction. It is performed in video sequence by subtracting the background image from the current frame. The foreground objects can be obtained by comparing the background image with the current video frame. By applying this approach to each frame in a video sequence tracking of any moving objects can be effectively achieved. This technique is commonly known as background subtraction or change detection. It is a very useful technique in video surveillance applications. Under noisy conditions the object has the same colour as the background. It is difficult to /13/$ IEEE

2 013 International Conference on Pattern Recognition, Informatics and Mobile Engineering (PRIME) 379 separate foreground objects and background. While processing the image, the noise can be removed by using suitable filters. Data flow diagram of a BGS algorithm is shown in the Fig 1. Here the belief area unit created as that the camera is mounted. Object is extracted by exploitation the steps of preprocessing, background modelling, foreground detection and post process. The input video frame is partitioned off into little blocks. A classification decision is made by whether the block belongs to the changes or not in each block. To this end, the invariant features from the image blocks are extracted. Then the images blocks are classified into two categories as foreground and background based on these features. The background model and the classifier should be adaptive when the background is time-varying. The background adaptive is risky. For an example, if a person sits for a long time or sleeps in the place the adaptive background model will consider the person part of the background. To solve this problem, the high-level knowledge obtained from object tracking with the low-level feature-based classification is fused. That is used for background update. The foreground may also still contain objects. To solve this, we develop a decision process based on a modified k means algorithm to detach these objects from the background. In the following sections, we explain the proposed background subtraction scheme in detail. Pre Processing BG Modelling Fore Ground Detection Figure 1. Data flow diagram of a typical BGS algorithm Post Processing μ t = α I t + (1- α) μ t-1 (1) Where I t, is the pixel s current value and, μ t-1, the previous average, α is an empirical weight often chosen as a trade- off between stability and quick update. Background modeling describes the kind of model used to represent the background in a frame. The simplest way to model the background is to acquire a background image which does not include any moving object. The K-means algorithm and its development algorithms are in the family of centre base clustering algorithms. This family consists of several methods: expectation maximization, fuzzy K-means and harmonic K-means. Template matching, Meanshift, Particle filter and K-means tracker are representative object tracking algorithms. Among them, the template matching method is the most conventional and often used for a high precision positioning of electric and mechanical parts. However, the algorithm doesn t achieve sufficient performance for the digital camera application. On the other hand, Mean-shift, Particle filter and K-means tracker are known to have high performance for scenes taken by a digital camera. However, Mean-shift is not robust enough for scale and appearance changes of an object. The computation cost of Particle filter is high so that it is difficult to apply for a digital camera. We selected K-means tracker as the object tracking algorithm because the algorithm is applicable for wide variation of scale and appearance change with reasonable computation cost. Partition based clustering methods create the clusters in a single step. One set of clusters only is created as output even though several sets of clusters may be created internally within among the assorted algorithms on the market. Since only one set of clusters is output, the users must input the desired number of clusters. In the given database of n objects, a partition based clustering algorithm constructs k partitions of the data such that an objective function is optimized. In these clustering methods some metric or criterion function is used to determine the goodness of any proposed solution. This measure of quality could be average distance between clusters or some other metric. One common measure of such kind is the squared error metric, which measures the squared distance from each point to the centroid for the associated cluster. Partition based clustering algorithms try to locally improve a certain criterion. The majority of them could be considered as greedy algorithms. These algorithms choose the best solution at each step and that may not lead to optimal results in the end. The best solution at each step is the placement of a certain object in the cluster for which the representative point is nearest to the object. This family of clustering algorithms includes the first ones that appeared in the Data Mining Community. III. PROPOSED METHOD K-means could be a fashionable algorithmic method for clustering. It partitions information set into k sets in a effective way. Here the membership of each data point is belongs to its nearest center, looking on the minimum distance. There square measure many ways to enhance the quality of K-means algorithmic method associated with many aspects. Standard K-means algorithm consists of four steps: initialization, classification, computational and convergence condition.the main contribution of this paper is a methodology to detach objects being moved. Detaching objects from the adaptive background is a challenging task because of the lack of automated reasoning about what is, and is not, a target object, as evidenced in a video sequence. S x1 x x,,.. n ()

3 013 International Conference on Pattern Recognition, Informatics and Mobile Engineering (PRIME) 380 Total range of clusters K and a dataset containing n objects (Xi) A set of K clusters Cj that minimizes the square error criterion. Begin M=1 Initialize K Prototypes Zj, j [1, K] // arbitrarily choose K objects as centers Repeat for i=1 to n do begin for j=1 to K do Compute D(X i, Z j )= X i -Z j ; (3) If D(X i, Z j )=min{d(x i, Z j )} X C then i j end if m= 1 then m = m+1 for j=1 to K do K c m X Zj j 1 Xi Cj J i z j 1 nj j i j i 1 n X J Calculate the mean value for each clusters K c m X Zj j 1 Xi Cj J i Until Jcm ( ) Jcm ( 1) s The complexity of K-means algorithm is Ö(ndk), where n is the total number of objects, k is the number of clusters, and d is the dimensions of datasets. Algorithm for Initial Threshold The process of the algorithm is contains n objects. (1) Select k objects randomly from the background frame as the initial threshold. () Repeat (3) and (4) until no object changes the class t belongs. (3) Compute the distances between each object by equation 3 & and all threshold, and if one object has the shortest distance from one centroid with respect to the other threshold then it has the same name as the centroid. All of these objects that have the same name belong to the same class. (4) Average all the vectors of objects belonging to the same class and form the new threshold as equation 5. The standard k-means algorithm automates between assigning the datapoints to their closest centroid (the E-step) and moving each centroid to the mean of its assigned data-points (the M-step)'. c (4) (5) Initialize K Clusters based on algorithm Choose Initial Centroids based on proposed algorithm Compute the Distance between each pixel and seed point Group the pixels to Minimum Distance Clusters Update Centroids Check for minimize error Final Thresholds Background Subtraction Based on Final thresholds Figure. Proposed Algorithms Because the standard k-means algorithm gets easily trapped in a local minimum and different initial threshold lead to different results, if we find certain initial threshold that are consistent with the distribution of data, then a better clustering can be obtained. The aim of k-means algorithm is to partition objects into several classes and to make the distances between objects in the same class closer than the distances between objects in different classes. So if certain threshold in which each centroid represents a group of similar objects can be obtained, we will find out the threshold consistent with the distribution of data. Let U be a data-point set. The initial threshold can be gotten by the following steps. Firstly compute the distances between each data-point and all of the other data-points in U. Secondly find out the two data-points between which the distance is the shortest and form a datapoint set AI which contains these two data-points, and then we delete them from the population U. Thirdly. compute distances between each data-point in AI and each data-point in U, find out the data-point that is closest to the data-point set

4 013 International Conference on Pattern Recognition, Informatics and Mobile Engineering (PRIME) 381 AI (i.e. of all distances, thy distance between this data-point and certain data-point in A1 is shortest), delete it from U and add it to AI. Repeat the third step till the number of data-point in A1 reaches certain threshold. Then we go to step two and form another data-point set till we get k data-point sets. Finally the initial threshold can be gotten by averaging all the vectors in each data-point set. The Euclidean distance is used in this paper. The distance between one vector X=(xl,x,...,xn) and the other vector Y=(yl,y,...,y n) is described as follows experimental results of proposed modified K-means method of background subtraction are represented in Fig 3, Fig 4 and Fig 5. Table 1 shows the quantitative results of various frames of proposed method with other BGS. In proposed K-means algorithm better results are obtained and the sensitivity, specificity and accuracy in background subtraction are better than other background algorithm. = (7) d(x,y)= (x 1 -y 1 ) +(x -y ) + +(x n -y n ) (6) (1) Suppose there are n data-points in the population U and we want to partition U into k classes. Set m=l. Then the algorithm is described as follows. Compute distances between each data-point and all of the other data-points in U, find the two data points between which the distance is the shortest and form a data-point set A m (l < m < k) which contains these two data-points, delete these two data-points from U. () Find the data-point in U that is closest to the data- point set A m, add it to A m and delete it from U. (3) Repeat step () till the number of data-points in Am reaches α where α = (0 < α <=1) (4) If m < k, then m = m+l, find another pair of data-points between which the distance is the shortest in U and form another data-point set A m and delete them from U then go to step () 5) For each A m (I < m < k) sum the vectors of data-points and divide the sum by the number of data-points in A m, then each data-point set outputs a vector and we select these vectors as the initial threshold. (6) Execute the process of the standard k-means algorithm from step. Fig 3 (a) = Accuracy = TN FP + TN TP + TN TP+TN+FP+FN Fig 3 (b) (8) (9) IV. EXPERIMENTAL RESULT Each pixel in a background subtraction method s classification was determined to be: true positive (TN) for a correctly classified foreground pixel, false positive (FP) for a background pixel that was incorrectly classified as foreground, true negative (TN) for a correctly classified background pixel, and false negative (FN) for a foreground pixel that was incorrectly classified as background. By the calculation of TP, TN, FP, and FN the different methods can be evaluated. Then the sensitivity, the specificity and accuracy was calculated by using the formula given in equation7, 8and9. Sensitivity is defined in equation 7; specificity is defined in equation 8 and accuracy is defined in equation 9. In the video frame sensitivity and specificity are the statistical measures of the performance of a binary classification test. The proportion of actual positives which are correctly identified is measured by sensitivity. The proportion of negatives which are correctly identified is measured by specificity. These two measures are closely related to the concepts of type I and type II errors. The experiment was conducted in this way for background subtraction by using different methods. The three Fig 3 (c) Fig 3(d) Figure 3 (a) Background reference image (b) Current frame (c) Proposed Fig 4 (a) Fig 4 (b) Fig 4 (c) Fig 4(d) Figure 4 (a) Background reference image (b) Current frame (c) Proposed

5 013 International Conference on Pattern Recognition, Informatics and Mobile Engineering (PRIME) 38 the dynamic environment with new objects in it. When comparing background subtraction with traditional method in real world, the experimental result shows that proposed approach is more accurate than other classical algorithms. Fig 5 (a) Fig 5 (b) Fig 5 (c) Fig 5 (d) Figure 5 (a) Background reference image (b) Current frame (c) Proposed TABLE I:COMPARISON OF THREE TEST RESULTS WITH OTHER BGS S.No Method Sensitivity Specificity Accuracy 1 BGS Proposed BGS Proposed BGS Proposed V. CONCLUSIONS The In this paper, the most attractive modified k-means algorithm is applied for detecting thresholds, but the standard background subtraction methods based on threshold cannot always give a good and stable detection. Experimental results show that selecting centroids by our algorithm can lead to a better background subtraction. The results on several sequences show that this algorithm is efficient and robust for REFERENCES [1] V. Mahadevan, and N. Vasconcelos, Spatiotemporal Saliency in Dynamic Scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 3, No. 1, pp , 010. [] C. Stauffer and W. E. L. Grimson, Adaptive background mixture models for real-time tracking, in Proc. IEEE Conf. Computer Visionand Pattern Recognition, 1999, pp [3] C. Stauffer and E. Grimson, Learning patterns of activity using realtime tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol., No. 8, pp , 000. [4] Luis Unzueta, Marcos Nieto, Andoni Cortés, Javier Barandiaran, Oihana Otaegui, and Pedro Sánchez, Adaptive Multicue Background Subtraction for Robust Vehicle Counting and Classification, IEEE Transactions on Intelligent Transportation Systems, Vol. 13, No., June 01. [5] Piccardi, M. Background Subtraction Techniques: a Review, Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Vol. 4, pp , 004. [6] Chen Zhang, Shixiong Xia, K-means Clustering Algorithm with Improved Initial center, in Second International Workshop on Knowledge Discovery and Data Mining (WKDD), pp , 009. [7] Koheri Arai, Ali Ridho Barakbah, Hierarchical K-means: an algorithm for Centroids initialization for k-means, department of information science and Electrical Engineering Politechnique in Surabaya, Faculty of Science and Engineering, Saga University, Vol. 37, No.1, 007. [8] L. Li, W. Huang, I. Y.-H. Gu, and Q. Tian, Statistical modeling of complex backgrounds for foreground object detection, IEEE Trans.Image Process., vol. 13, no. 11, pp , Nov [9] D. Culibrk, O. Marques, D. Socek, H. Kalva, and B. Furht, Neuralnetwork approach to background modeling for video object segmentation, IEEE Trans. Neural Netw., vol. 18, no. 6, pp , Dec.007 [10] A. Elgammal, R. Duraiswami, D. Harwood, and L. S. Davis, Background and foreground modeling using nonparametric kernel density estimation for visual surveillance, Proc. IEEE, vol. 90, no. 7, pp , Jul. 00