Segmentation of moving object motion structure

SEGMENTATION OF MOVING OBJECT MOTION STRUCTURE 1

Neeraj Kumar Pandey, 2 Dr. Brij Mohan Singh,

1

Assistant Professor, Deptt. Of CS, College of Engineering Roorkee, (Uttrakhand)-India.

Associate Professor , Deptt. of IT, College of Engineering Roorkee, (Uttrakhand)-India.

2

Abstract: In video analysis moving object detection and tracking is considered as most challenging and essential task for many video and image based applications like video surveillance, biometric identification, satellite imaging, terrain analysis, augmented reality, face/human detection, user tracking, gesture recognition, behavior analysis and traffic analysis etc. The focus of this paper is segmentation of motion structure of moving object as video-based motion detection and analysis technique. We aim to implement a robust algorithm for segmenting moving object motion structure that can detect object in variety of challenging real world scenarios while maintaining the temporal dependencies between objects. We organize the video into frames in which the background is stationary. The paper presents work within four steps namely preprocessing, foreground segmentation, object tracking and segmentation of motion structure.

Keywords: Object detection and tracking, frames, Preprocessing, foreground segmentation, Segmentation of motion.

I. INTRODUCTION Moving object detection has got wide application in video analysis, surveillance systems, in natural science and in various other fields. Object tracking has received attention of researchers where security is the primary requirement and it is not possible for human operators to monitor every surveillance system continuously. There are places of public domain where object tracking has wide application [1-10] for security purpose such as in military, banking sector, biometric identification, shopping areas, Institutes, train stations and airports. Various other important applications include road traffic analysis, air traffic control, satellite imaging, terrain analysis, augmented reality and robotics.

An automated system to detect motion in video will facilitate many applications and thus has received great interest from both research communities and industries. With the recent advancements in computer vision it might be able to reasonably claim that there are feasible solutions are available to addressing the robust tracking of single target, however, simultaneously analyzing multiple targets motions and tracking them in video stay as one of the most challenging problems in computer vision [1].

Our motivation in reviewing this problem is to create a visual surveillance system with real time moving object detection, classification, and tracking and activity analysis capabilities. Motion structure of moving structure represent vital information about the movement, activity, and many more that can be used for knowledge extraction further in various applications. For security reasons surveillance camera is start showing their presence around every corner of our daily life. A robust motion detection method is requiring that is to be capable of handling foreground commonage, shadows, and moving backgrounds. The method needs to updates the background model

@IJRTER-2016, All Rights Reserved

150

International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 02, Issue 08; August - 2016 [ISSN: 2455-1457]

continuously to maintain high quality segmentation over long periods of time and it also detect and track multiple moving objects in adverse lighting conditions.

The detection and analysis of motion in video provides valuable information that can be used with number of applications. Through this analysis we can analyze traffic, identify human behavior, used to optimize performances in sports, introduces new interaction method in gaming console, Law enforcement, intelligence agencies and the security industry are analyzing human motion in surveillance videos to identify illegal or suspicious behavior or to identify individuals in real time.

An activity specific video surveillance system may be a solution for many of the above problems.

The analysis of motion will allow for robots that can work alongside humans and interact with humans in a natural way. One of the main driving applications of motion analysis has been automated analysis of moving object present in video which has been partly motivated by the focus on security and prevention of terrorist attacks increased in recent years. In literature researchers have proposed various techniques to generate short representation of video data that require respectively less time to monitor and also take less storage space. While we study video analysis we divide methods developed in the literature broadly into two categories: Static image based summarization to generate sketch of all activities in original videos and dynamic content based video summarization [1] [2].

In static image based method each shot is represented by key frames are selected to generate representative image. Some of the examples of static image based summarization are video mosaic in which video frames are found using region of interest and stitch together to form a resulting video. Another form is video collage in which single image is generated by arranging region of interest on a given canvas. Storyboards and narratives are some more basic form of image based summarization [3]. However static image based method generate resulting summary in less space but here it does not take care of temporal dependencies between important event and researcher also want to maintain the resulting summary visually more appealing than watching static images. As an example of dynamic content based video summarization method Video synopsis condenses video content in both spatial and temporal dimensions and present short video that help in fast browsing.

Video synopsis presents some limitations as it requires large memory area to store foreground and background regions. While video synopsis save space it does not maintain consistency between different objects, also the pleasing effect of video is highly depend upon the length of final synopsis [4]. Some more examples of dynamic methods are video fast-forward, video skimming, space–time video montage method, video narrative where selected frames are arranged in order to form a highly condense video [5]. The segmentation of motion structure of moving structure serve as a compact form of representation of information regarding moving objects. Motion structure of an object is used to generate various information retrieval tasks such as direction of movement, traffic flow, representative trajectory and many more [6][7][8]].

Basically this involves moving object segmentation which is also termed as foreground segmentation from the stationary region which consider as background. While traditional methods of moving object segmentation using Optical flow method, Background subtraction and Background estimation and temporal differencing provide satisfactory results to detect single moving objects but in case of multiple objects stream and in poor lighting conditions noise is produced. The goal of this paper is to apply a combined method of frame differencing and Gaussian Mixture model (GMM) for foreground segmentation and an efficient algorithm for tracking the segmented object across the frame and last but most important task is the segmentation of motion structure of tracked objects.

@IJRTER-2016, All Rights Reserved

151

International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 02, Issue 08; August - 2016 [ISSN: 2455-1457]

II. SYSTEM ARCHITECTURE Segmentation of motion structure of moving objects is start with detecting moving object in every frame and then tracking these objects across the frames. Basically following four key steps are followed in this work: i. Preprocessing: Represent video in the form of individual frame and apply filter to remove noise and convert frames into gray scale images. ii. Foreground Segmentation: Detection of moving objects as foreground is performed at lowest semantic level. iii. Object Classification and Tracking: At this level object of interest is been segmented out from the foreground and we track the object like human or vehicle across the frames. iv. Segmentation of motion structure: Based on low-level semantic knowledge extracted from earlier stages, the more sophisticated understanding of the videos focuses on identifying object actions, understanding of the behaviors and activities of each object by segmenting motion structure of individual object. The making of video analysis systems “smart� requires fast, reliable and robust algorithms for moving object detection, classification, tracking and activity analysis. A basic block diagram of system is given below in Figure 1.2.

Input video PREPROCESSING

FOREGROUND SEGMENTATIONN

TRACKING OBJECT

MOTION STRUCTURE SEGMENTATION

ACTIVITY ANALYSIS

Figure 1.2: Basic block diagram of system

II. A. Preprocessing: The segmentation of motion structure of moving object from video cannot be furnished in a single phase as it incorporated steps that need sequence of sub tasks. The implementation of this thesis is implemented in four sub task those are consider as an independent unit where output of one phase is served as input of next. The reason behind dividing the main problem in sub problems is number of methods available at each step. Since the complexity of foreground segmentation process is depend upon whether the input frame is noisy or not.

Preprocessing phase here is simple but important phase on which the results of further steps are depends. Video is nothing but a sequence of frames arrange in a manner that when passes with some minimum speed as frame per second in front of human eyes it seems as continuous shot. The video processing needs to separate frames as it apply image processing on individual frames. Video capture using surveillance cameras or camera having low resolution may contain frames with noise in the form of uneven illumination or brightness. Noisy frame can be produces false detection and

@IJRTER-2016, All Rights Reserved

152

International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 02, Issue 08; August - 2016 [ISSN: 2455-1457]

can degrade the performance of detection algorithm. Before applying segmentation median filter is apply on each frame to remove noise present in frames.

Median Filter: Filters are used to remove noise present in images. Median smoothing filter is applied to reduce noice and luminance change. Median filter determine the principal of an odd pixel window W, window size of each pixel arranged according to gray, middle gray value instead of original F(i,j) the gray value, gray value as the center of the window G(i,j) is given in equation 3.1.

G (i, j) =median {F (i − k, j − l), (k, l ∈ W)}

(2.1)

Where W is the selected window size, F (i-k, j-l) for the window W of the pixel gray value, usually an odd number of the pixel in the window.

II. B. Foreground Segmentation: In computer vision segmentation is the process which divides a digital image into multiple segments (sets of pixels) also treated as blobs in literature.

The main aim of segmentation is to simplify and/or change the representation of an image frame into something which is more meaningful and easier to analyze. Segmentation of image is typically used to detect/locate objects and boundaries in an image frame. More precisely, segmentation of an image is carried out to assign a label to each pixel in an image such that pixels with the similar label share certain visual Characteristics.

First of all potential moving objects are detected by using local change in contrast over time.

Although this may contain false detection which is removed by applying the constraints of size and Motion prediction and also spatial nearest neighbor data association is used to suppress false alarm due to minor contrast change. After this program is executed we get a contrast image which contains the moving objects. This contrast image is converted to binary image. This is carried out by implementing thresholding and performing blob-analysis and other morphological operations on each foreground image. Then object tracking is carried out.

Automatic video segmentation aims the separation of moving objects also termed as foreground from the background and identification of accurate outlines of the objects. The performance of tracking algorithms usually depends also on the measure characterizing the similarity or dissimilarity between the two subsequent images/video frames.

Video segmentation has two major types: spatial segmentation and temporal segmentation. Spatial segmentation is based on the digital image segmentation approach. Digital image segmentation is partition an image locally or globally as bi-level or higher level into different regions as of interest belonging to the image. In the thresholding process depending on their values individual pixels in an image are marked as “foreground” pixels if the value is greater than some threshold value (assuming an object to be brighter than the background) else the pixels are marked as "background" pixels.

Moving object detection is the basic step for further analysis of video. It handles segmentation of moving objects from stationary background objects [10].

Commonly used techniques for Object Detection are I. Background subtraction[11] II. Optical flow[12] III. Temporal differencing[13]

@IJRTER-2016, All Rights Reserved

153

International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 02, Issue 08; August - 2016 [ISSN: 2455-1457]

II.B.I Background Subtraction Background subtraction is a commonly used class of techniques for segmentation of foreground objects of interest in a scene for applications such as surveillance. It compares current i m a g e with an estimate background image if it contained no objects of interest. The areas of the image plane where there is a significant difference between the current and background images indicate the location of the objects of interest. The name “background subtraction" comes from the simple technique of subtracting the observed image from the estimated image and thresholding the result to generate the non-stationary foreground objects. Here we implement several techniques which are representative of this class, and compare three important attributes of them: how the object areas are distinguished from the background; how the background is maintained over time; and, how the segmented object areas are post-processed to reject false positives, etc. Three algorithms were implemented to evaluate their relative performance under a variety of different operating conditions. With this, some conclusions can be drawn about what features are important in an algorithm of this class.

To localize the foreground objects in a scene background subtraction methods are applied in many systems for segmentation. For background subtraction both pixel-based methods and region-based methods have been proposed. Pixel-based methods classify each pixel as foreground or background individually whereas region-based approaches classify a pixel based on the region surrounding it or alternatively classify the whole region in one step.

Both types of background subtraction can incorporate temporal information to improve segmentation. In general terms, region-based approaches produce more desirable segmentation results but at the expense of increased computational complexity. However, when looking at background subtraction as a preprocessing step to motion analysis a fast pixel-based method is often preferred to allow more to spare for more complex processing in the later steps. The pixelbased methods may produce more noisy segmentations but rather simple filtering can significantly reduce the amount of noise and perfect segmentations are rarely needed in the subsequent processing steps of the motion analysis.

The background subtraction method has two different update mechanisms to handle gradual and rapid changes respectively. To handle gradual changes the background pixels are continuously updated by taking a weighted average of the values of the observed image pixels and the new pixel values. To avoid updating based on falsely classified background the continuous update process is only applied to stable background, i.e. pixels that have been classified as background for a period of time. This update procedure does not handle rapid changes in the background and a different update process is therefore also applied. By allowing new pixel values to be included in the background model at runtime it is possible to adapt to new background after a short training period. If cars are parked in the scene or the weather changes the appearance of parts of the scene (e.g. by wind or rain), then new pixel will be observed during some training period which means that they represent true background and not foreground objects.

Typically following sequence of calculation is been followed by a background subtraction system I.Background Model Initialization: Initially it requires frame sequence to generate background reference image. Here it is consider that video sequence is start with absence of moving object. The simplest way to generate reference image is time- averaged method is given in equation 3.2

@IJRTER-2016, All Rights Reserved

154

International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 02, Issue 08; August - 2016 [ISSN: 2455-1457]

Where BMN (x, y) is the intensity of pixel (x, y) of the background model, Im(x, y) is the intensity th of pixel (x, y) of the m frame, and N is the number of frames used to construct the background model. It has been shown in figure 3.1 that N = 100 is a good choice for N. II.Background Subtraction After obtaining the initial background model, we need to obtain the difference between the current frame and the background model as given below,

Where BMt(x, y) is the intensity of pixel (x, y) of the background model at time t, and It(x, y) is the intensity of pixel (x, y) in the current frame at time t.The difference Dt(x, y) is then compared to an adaptive threshold Thad for foreground background pixel classification. Thad is obtained iteratively using the histogram of the difference frame Dt(x, y) to account for frame-to-frame changes in the background. If Dt(x, y) < Thad, the pixel is classified as a background pixel. If Dt(x, y) â&#x2030;Ľ Thad, then it is a foreground pixel.

III.BACKGROUND MODEL UPDATE The background model must be updated for every frame in order to accommodate for background dynamics such as illumination changes and waving tree leaves.

This method suffers from many problems and requires a training period absent of foreground objects. The motion of background objects after the training period and foreground objects motionless during the training period would be considered as permanent foreground objects. In addition, the approach cannot cope with gradual illumination changes in the scene. These problems lead to the requirement that any solution must constantly re-estimate the background model. An example of background differencing is shown below in Figure 2.2(a)-(f).

(a)

@IJRTER-2016, All Rights Reserved

(b)

(c)

155

International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 02, Issue 08; August - 2016 [ISSN: 2455-1457]

(d)

(e)

(f)

Figure 2.2: (a) Original frame (b) Foreground mask (c) Segmented moving object

Above figure shows the example of background differencing over two consecutive frames of video from dataset available online at i-lids 2007 [19]. Figure 2.2 (a) & (d) are original frame 1187 and 1188 from video and(b) & (e) are the foreground mask of moving object in these frames and (c) and (f) shows the moving objects present in frame after segmentation respectively.

II.B.II. Optical Flow In a video frame, the field of motion vector per pixel or sub-pixel is called optical flow. There are many a methods for computing optical flow among which few are partial differential equation based methods, gradientconsistency based methods and least squared methods.

The model used here is based on an optical flow estimation technique which estimates the motion vectors in each frame of a video sequence. Then thresholding followed by morphological closing on the motion vectors produces binary feature images. The model further locates objects in each binary image using the Blob Analysis. The Draw Shapes block draws a green rectangle around the objects that moves beneath the white line.

In the area of optical flow research, motion estimation has developed as one major aspect. The optical flow field is more or less similar to a dense motion field which is computed in motion estimation. Optical flow can be used not only for the determination of the optical flow field itself, but also of its requirement in the estimation of the three- dimensional nature and structure of a scene, and the 3D motion of objects and the observer relative to the scene.

Robotics researchers have been widely using optical flow in areas as diverse as: object detection and tracking, movement detection, image dominant plane extraction and robot navigation etc. Moreover, Optical flow information has also been identified as a potential method for controlling micro-air vehicles.

For Optical flow estimation, there exist two well-known methods i.e., the Horn-Schunck[5] algorithm and the Locase-Kanade method. While the Horn-Schunck method (global method) is based on smoothness in flow over the entire image, the Locas-Kanadeâ&#x20AC;&#x2122;s method is a local method.

The Hornâ&#x20AC;&#x201C;Schunck algorithm is preferred because it produces a high density of flow vectors, i.e. the m i s s i n g flow information in internal parts of objects is filled in t h e form o f motion boundaries. However, it has its demerits too. It tends to be more sensitive to local noise than other methods.

@IJRTER-2016, All Rights Reserved

156

International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 02, Issue 08; August - 2016 [ISSN: 2455-1457]

The optical flow of a video frame is a field of motion vector per pixel or sub-pixel. Multiple methods allow computing the optical flow among which partial differential equation based methods, gradient consistency based techniques and least squared methods [5].

In this model we have used an optical flow estimation technique to get an estimation of the motion vectors in each frame of the video sequence. Then the required moving object is detected by a program block and converted into binary image. This is carried out by implementing thresholding and performing blob-analysis and other morphological closing on each foreground image. Then object tracking is carried out by another program.

The optical flow is a differential method. It is declared that optical flow is based on the idea that the brightness is continuous for most of the points in the image, neighboring points have approximately the same brightness. In OF method, given scene have continuous objects over which brightness varies smoothly. In successive frames, pixels belong to the same objects have the same brightness similar to the conservation of mass law in fluid dynamics. The continuity equation for the optical term by omitting the second order terms is given below in equation 2.4

Where g is the brightness function and u is the velocity vector. In one- dimensional case, the above equation takes the simple form which one can directly determine one-dimensional velocity as given in equation 2.5.

Where

This trackerâ&#x20AC;&#x2122;s algorithm detects interest points in the first given frame and propagates them to succeeding frames based on local appearance matching. Propagated unreliable points are discarded and replaced with new points, and the output of algorithm is a set of these reliable points (e.g. features of tracked points). An example of foreground segmentation using optical flow is shown below in figure 2.3 (a) â&#x20AC;&#x201C; (f). Where figure 2.3 (a) and (d) are original frame sequence 1350 and 1351 from i-lids dataset and (b) and (e) are foreground mask using optical flow and (c) and (f) are segmented moving object of both frame respectively.

(a)

@IJRTER-2016, All Rights Reserved

(b)

(c)

157

International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 02, Issue 08; August - 2016 [ISSN: 2455-1457]

(d)

(e)

(f)

II.B.III. TEMPORAL DIFFERENCING Temporal differencing makes use of the pixel-wise difference between two or three consecutive frames in video imagery to extract moving regions. It is a highly adaptive approach to dynamic scene changes; however, it fails in extracting all relevant pixels of a foreground object especially when the object has uniform texture or moves slowly. When a foreground object stops moving, temporal differencing method fails in detecting a change between consecutive frames and loses the object. Special supportive algorithms are required to detect stopped objects [7].

Frame difference method, is also known as the adjacent frame difference method, the image sequence difference method etc. It refers to a very small time intervals Î&#x201D;t of the two images before and after the pixel based on the time difference, and then thresholding to extract the image region of the movement.

Frame differencing is the simplest form among of background subtraction method. It is done by comparing the pixel from the current frame and the previous image as the background.

The current frame will be subtracted from the previous frame and the pixel difference is compared with a threshold as given in equation 2.7.

Threshold is a value to determine whether the given pixel is foreground or background. If the absolute difference between the two frames is greater than the threshold, the pixel will be assumed as part of the foreground. Otherwise, it is considered as background pixel. The main consideration for this technique is on how to determine the suitable threshold value.

III. OBJECT TRACKING As detection phase represent each object in the form of a point over the spatial domain the tracking is the process to assign consistent label to each detection point across the frame. Numerous approach for object tracking has been proposed that are depend upon object representation, image features and motion, appearance and shape of the object.

Tracking may be defined as following the trajectory of a moving object across frames as it moves around the scene. Consistent labels are assigned to the tracked objects in each frame of a video.

Further based on the tracking domain, a tracker can give useful information such as movement, shape, and orientation of the object under interest.

Object tracking become a complex task due noise in images, complex object motion, articulated nature of non-rigid objects, object occlude each other, object occluded by structure, and real-time

@IJRTER-2016, All Rights Reserved

158

International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 02, Issue 08; August - 2016 [ISSN: 2455-1457]

processing requirements. Tracking can be simplified by making some assumptions or imposing some constraints on the motion or appearance of the object. In almost all tracking algorithms, object motion is assumed to be smooth with no abrupt changes in between. Prior knowledge about the object size, number, appearance, shape and motion can also help in its tracking. A number of methods for object tracking have been proposed.

After completing the segmentation of moving object the proper tracking of corresponding object across the frame is important and challenging task. There are number of ways exist in literature to represent segmented objects such as bounding box, centroid, and mask. Although all methods specified above are interrelated as centroid can be computed through bounding box coordinates and mask is actual appearance of object inside bounding box after removing the extraneous region.

Tracking is a difficult task due to abrupt change in object motion, illumination change, and objects occlusion that causes miss detection of object [6].

After segmentation of moving objects next task is creating match matrix where we maintain the last location of each track. When in any frame we detect any object during segmentation we calculate the Euclidean distance between detection point and point in match matrix. The corresponding detected point is assigned the track with minimum distance from match matrix.

Track 8 Track 11 Track 12 Track 16

DETECTION 1 31 44 2 24

DETECTION 2 3 26 15 23

DETECTION 3 25 3 45 7

DETECTION 4 28 20 42 28

Table 3.1: Match matrix used for tracking

IV. BACKGROUND SUBTRACTION COMBINED TEMPORAL DIFFERENCING Foreground segmentation is basically used as a primary step of detection of moving objects that can be further processed and analyzed. This is required for obtaining knowledge representation, activity analysis, classification or array tubes can be arranged over spatial domain for displaying resulting paper as per user requirement. Existing methods successfully segment out video showing single moving object but face many challenges while segmenting multiple targets such as: a) tracking of individual object in multiple object environment is not easy; b) objects may overlap over space; c) object may stop moving and become part of background, d) background object may start moving, e) number of tubes may become large in crowded video [14].

In this paper, Background modeling and foreground segmentation is performed using background subtraction combined temporal difference based on adaptive online Gaussian mixture model for detecting moving objects. Adaptive online Gaussian mixture model was presented by Stauffer and Grimson [13] that gives promising result and deal successfully with lighting change, slow moving objects and repetitive motion of scene elements. These are the guiding factors in our choice of model and update procedure. The mixture of K Gaussian distributions is used to represent result of foreground segmentation is set of pixels called blobs which are region of interests. Each connected group of pixels represents a moving object. Some pixels regions of minimum size also generated while segmenting foreground which is termed as noise. The morphological operations like erosion and dilation removes these small size blobs thus noise. Some more blobs of size less than minimum allowable are remove using size property to segment only large size moving objects.

@IJRTER-2016, All Rights Reserved

159

International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 02, Issue 08; August - 2016 [ISSN: 2455-1457]

Morphological operations like opening and closing [8] with a 5x5 square structuring element are performed to connect components and fill holes in segmented objects. This helps in isolating individual objects and reducing false positive due to minimal sized false detection. Then the connected components are labeled using (8-connectivity) [8] to separate out individual object.

V. RESULT To evaluate the performance of our approach we have performed experiments on video downloaded from i-Lids dataset for (AVSS 2007) [17], Vision Traffic video from Matlab database and KTH [18].The details of videos used for experiments is shown in table 6.1.

Video Vision Traffic KTH i-lids AVSS

Resolution 160x120 720x576 720X576

fps 25 fps 25 fps 25 fps

Size 1.67 MB 104 MB 2.8 MB

Table 5.1: Videos used in the experiment Source Resolution Frame Rate

We have been able to generate video array for the frame sequence containing multiple number of moving objects. Presently we could isolate three moving objects. The result of experiment is shown in Table 5.2.

Source KTH AVSS i-lids AVSSi-lids Vision Traffic

MovingObject density 1 2 3 3

Frames Tested 400 300 250 150

Result Frames 400 290 225 142

Result 100% 96.66% 90% 94.66%

Table 5.2: Results of experiments

Figure 5.1 and figure 5.2 shows an example of three frame sequences with one moving object and two moving object with successful foreground segmented frame sequence and the resulting array sequence of first and second moving objects respectively. Figure 6.3 shows segmented moving object in single frame so it can be used as a summary of object motion in video by representing object in single frame.

Figure5.1: Segmentation and tracking of single moving object between three consecutive frames

@IJRTER-2016, All Rights Reserved

160

International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 02, Issue 08; August - 2016 [ISSN: 2455-1457]

Figure 5.2 Segmentation and tracking of two moving object between three consecutive frames

Figure 5.3: representation of object in video in single frame

The motion structures of moving object in two video sequences are shown below in Figure 5.4.

(a)

(b)

Figure 5.4 motion structure moving object (a) i-lids 1250-1467 (b) vision traffic 0-435

VI. CONCLUSIONS In this work an approach to generate motion structure of moving object is presented and discussed.

Background subtraction using GMM combined frame differencing is used for foreground segmentation that gives promising result with some morphological operations and filtering. Interframe difference between centroid of moving object is performed for labeling and tracking of moving objects. Parameters used with array is important for understanding the inter frame motion of individual objects. This approach of array generation is applicable in video analysis and video synopsis. It may be useful in future to process video in distributed environment.

@IJRTER-2016, All Rights Reserved

161

International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 02, Issue 08; August - 2016 [ISSN: 2455-1457]

REFERENCES 1.

2.

3.

4.

5.

6.

7.

8.

9.

10.

11.

12.

13.

14.

15.

16.

17.

J. Cao, X. Sun, and L. Hao(2013). ‘Surveillance video synopsis based on moving object matting using non interactive grabcut’. The Open Automation and Control Systems Journal, 5, 113-118, 2013.

W. Fu, J. Wang, L. Gui, H. Lu, and S. Ma(2014). ‘Online video synopsis of structured motion.’ In journal of Neurocomputing, 135(5),2014.

Y. Fu, Y. Guo, Y. Zhu, F. Liu, C. Song, and Z.-H.Zhou(2010). ‘Multi- view video summarization.’ IEEE Transactions on Multimedia, 12(7):287–296, November 2010.

Y. J. Lee, J. Ghosh, and K. Grauman(2012). ‘Discovering important people and objects for egocentric video summarization.’ In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2012., pages 1346 – 1353. IEEE, June 2012.

L. Li, K. Zhou, and G.-R. Xue (2011). ‘Video summarization via transferrable structured learning.’ In Proceedings of the 20th international conference on World wide web, pages 287–296. ACM, March 2011.

T. Mei, L.-X. Tang, J. Tang, and X. S. Hua(2010). ‘Near-lossless semantic video summarization and its applications to video analysis.’ ACM Transactions on Multimedia Computing, Communications and Applications, 2(3), May 2010.

C.W. Ngo, Y.-F. Ma, and H. Zhang(2003). ‘Automatic video summarization by graph modeling.’ In Proceedings of Ninth IEEE International Conference on Computer Vision, pages 104–109. IEEE, October 2003.

Y. Pritch, A. Rav-Acha, and S. Peleg (2008).‘Nonchronological video synopsis and indexing.’ In IEEE Transaction on Pattern Anal. Mach. Intell, pages 1971–1984. IEEE, October 2008.

R. C. Gonzalez(2010). ‘Digital image processing using matlab.’ Tata McGraw- Hill Education.

B. Karasulu(2010). ‘Review and evaluation of well-known methods for moving object detection and tracking in videos.’ Journal of Aeronautics and Space Technologies, 4(4), July 2010.

C. Schuldt, I. Laptev, and B. Caputo(2007). ‘Recognizing human actions: A local svm approach.’ In Proceedings of the 17th International Conference on Pattern Recognition, 2007.

C. Stauffer and W. E. L. Grimson(2000). ‘Learning patterns of activity using real-time tracking’. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), July 2000.

K. SuganyaDevi, N. Malmurugan, and R. Sivakumar(2012). ‘Efficient foreground extraction based on optical flow and SMED for road traffic analysis’. International Journal of Cyber-Security and Digital Forensics(IJCSDF), 1(3), November 2012.

P. Spagnolo, T. Orazio, M. Leo, and A. Distante(2006). ‘Moving object segmentation by background subtraction and temporal analysis.’ Image and Vision Computing, 24, January 2006.

C. Suliman, C. Cruceru, and F. Moldoveanu(2010). ‘Kalman filter based tracking in an video surveillance system’.

In International Conference on Development and Application Systems,, pages 54–58, May 2010.

S. zheng Wang, Z. yuan Wang, and R. min Hua(2013). ‘Surveillance Video Synopsis In The Compressed Domain For Fast Video Browsing’. Journal of Visual Communication and Image Representation, 24(28), November 2013.

Schindler, Konrad(2014). ‘Continuous Energy Minimization for Multi-Target Tracking.’ IEEE Transactions On Pattern Analysis And Machine Intelligence 36.1 (2014).

@IJRTER-2016, All Rights Reserved

162