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IORD Journal of Science & Technology E-ISSN: 2348-0831 Volume 1, Issue VI (SEPT-OCT 2014) PP 25-29 IMPACT FACTOR 1.719 www.iord.in

TRACKING OF SCENE IN VIDEO BY USING JOINT COLOUR AND TEXTURE HISTOGRAM METHOD Mr. Swapnil S. Rajurkar [1] Miss. Mamta Sarde[2] Department of Elecronics &Communication Engineering , Abha Gaikwad-Patil College of Engineering & Technology,Nagpur(MH) swapnil.rajurkar@gmail.com, mmsarde@gmail.com

ABSTRACT :

Video categorization needs the economical segmentation of video into scenes. Object pursuit is one in

every of the key technologies in intelligent video police work and the way to describe the moving target could be a key issue. a unique object pursuit rule is conferred during this paper by victimization the joint color texture bar graph to represent a target then applying it to the mean shift framework. The video is initial segmental into shots and a collection of key-frames is extracted for every shot. Typical scene detection algorithms incorporate time distance in an exceedingly shot similarity metric. within the technique we have a tendency to propose, to beat the problem of getting previous data of the scene period, the shots square measure clustered into teams primarily based solely on their visual similarity and a label is assigned to every shot in line with the cluster that it belongs to. Then, a sequence alignment rule is applied to discover once the pattern of shot labels changes, with the exception of the traditional color bar graph options, the feel options of the article are extracted by victimisation the native binary pattern (LBP) technique to represent the article. the foremost uniform LBP patterns square measure exploited to create a mask for joint color-texture feature choice. Compared with the normal color bar graph primarily based algorithms that use the entire target region for pursuit, the projected rule extracts effectively the sting and corner options within the target region, that characterize higher and represent a lot of robustly the target. The experimental results validate that the projected technique improves greatly the pursuit accuracy and potency with fewer mean shift iterations than customary mean shift pursuit. Experiments on TV-series and flicks additionally indicate that the projected scene detection technique accurately detects most of the scene boundaries whereas protective an honest trade-off between recall and preciseness.

INTRODUCTION

Video surveillance systems have long been in use to monitor security sensitive areas. The history of video surveillance consists of three generations of systems which are called 1GSS, 2GSS and 3GSS [36]. The first generation surveillance systems (1GSS, 1960-1980) were based on analog sub systems for image acquisition, transmission and processing. They extended human eye in spatial sense by transmitting the outputs of several cameras monitoring a set of sites to the displays in a central control room. They had the major drawbacks like requiring high bandwidth, difficult archiving and retrieval of events due to large number of video tape requirements and difficult online event detection which only depended on human operators with limited attention span. The next generation surveillance systems (2GSS, 1980-2000) were hybrids in the sense that they used both analog and digital sub systems to resolve some drawbacks of its predecessors. They made use of the early advances in digital video processing methods that provide assistance to the human operators by filtering out spurious events. Most of the work during 2GSS is focused on real time event detection. Third generation surveillance systems (3GSS, 2000- ) provide end-to-end digital systems. Image acquisition and processing at the sensor level, communication through mobile and fixed heterogeneous broadband networks and image storage at the central servers benefit from low cost digital infrastructure. Unlike previous generations, in 3GSS some part of the image processing is distributed towards the sensor level by the use of intelligent cameras that are able to digitize and compress acquired analog image signals and perform image analysis algorithms like motion and face detection with the help of their attached digital computing components. The ultimate goal of 3GSS is to allow video data to be used for online alarm generation to assist human operators and for offline inspection effectively. In order to achieve this goal, 3GSS will provide smart

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IORD Journal of Science & Technology E-ISSN: 2348-0831 Volume 1, Issue VI (SEPT-OCT 2014) PP 25-29 IMPACT FACTOR 1.719 www.iord.in systems that are able to generate real-time alarms defined on complex events and handle distributed storage and content-based retrieval of video data.

II.

METHODOLOGY AND IMPLEMENTATION

Object trailing will be outlined because the method of segmenting associate object of interest from a video scene and keeping track of its motion, orientation, occlusion etc. so as to extract helpful data. Object trailing, in general, could be a difficult downside. Difficulties in trailing objects will arise attributable to abrupt object motion, ever-changing look patterns of the thing and therefore the scene, nonrigid object structures, object-to-object and object-to-scene occlusions, and camera motion. Object trailing in video in 2 ways that, initial whereas reading the .avi file and another mmreader file, currently initial discuss the avi file the AVIFILE produce a replacement AVI file associated format is: AVIOBJ = AVIFILE (FILENAME) creates an AVIFILE object aviobj with the default parameter values. If name doesn't embody associate extension, then '.avi' are used. Use avifile/close to shut the file opened by avifile. The flow of technique as shown in following 

Read the .avi file.



Detect shot and Key Frame extraction



Features Extraction of object in scene frame using joint color and texture histogram.



Represent and track the target.



Finally detect the scene with target detection

START

Input the .avi video file

Number of key frame extraction Feature extraction from scene frame

Apply joint color texture histogram method

Target detection in scene frame Sequence alignment of all scene frame

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STOP

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IORD Journal of Science & Technology E-ISSN: 2348-0831 Volume 1, Issue VI (SEPT-OCT 2014) PP 25-29 IMPACT FACTOR 1.719 www.iord.in

Fig. Flowchart diagram

III.

Joint Color & Texture Histogram

In this paper, we tend to gift a object chase system with period moving object detection, classification and chase capabilities. within the projected system moving object detection is handled by the employment of associate adaptative background subtraction theme that dependably works in indoor and out of doors environments. we tend to conjointly gift 2 alternative object chase schemes, whereas reading the avi video and mmreader strategies for performance and detection quality comparison. the scene Detection in videos born-again into numbers of frames victimisation cluster formula. Scene with object detection in videos victimisation Sequence alignment and joint color &amp; texture bar graph the video is variety of range of shot therein shot the numbers of frame created then used Spectral cluster formula is used to extract the key frames of the corresponding shots. Next shots ar sorted with reference to their Visual similarity and labeled in keeping with the cluster they're appointed then range of Scene and target segmentation apply native binary pattern (LBP) Schema to represent the target texture options then propose a options extraction of object in scene frame victimisation Joint color &amp; texture bar graph technique for a additional distinctive and effective approach track the target illustration. Finally, a sequence alignment formula is enforced to spot high dissimilarities between ordered windows of shot labels then scene with target detection within the frames. Now, all the frames with target detection ar born-again to create same video. To perform key-frame extraction the video frames of an attempt ar clustered into teams victimisation associate improved spectral cluster formula. In earliest analysis the video is divided into shots then key frame extraction and direct used Sequence Alignment formula to create scene above all frame however currently implement the advance idea video is divided into shots and Spectral cluster formula is used to extract the key frames of the corresponding shots then options extraction of object in scene frame victimisation joint color and texture bar graph to represent track the target then finally used sequence alignment formula then within the output shows scene and target detection. during this approach scene with object detection victimisation Video victimisation technique Sequence Alignment and Joint Color &amp; Texture bar graph.

IV.

RESULT ANALYSIS :

during this chapter we tend to gift the take a look at setting and also the experimental results of our algorithms. Test Application and System We enforced a video player application to check our algorithms. The video player will play video clips keep in compressed and uncompressed AVI format. in depth and representative experiments ar performed parenthetically and testify the projected joint color-texture model primarily based mean shift chase formula as compared with the mean shift chase with look models. The videos of various scenes, together with the one that has similar target/background colours, ar utilized in evaluating the performance of various algorithms.within the chase system

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IORD Journal of Science & Technology E-ISSN: 2348-0831 Volume 1, Issue VI (SEPT-OCT 2014) PP 25-29 IMPACT FACTOR 1.719 www.iord.in implement 2 technique initial whereas reading .avi file and another whereas reading mmreader file. currently the primary experiment is on a .avi video sequence therein solely avi file browse within the matlab, video of medication table with 119 frames of abstraction resolution 352×240. The chase target is that the moving head. The target is initialized as an oblong region of size 29×39. Since there ar distinctive color variations between the target (the head of the player) and also the background, we tend to solely show the experimental results by the projected technique in Fig. .

Fig . Tracking results of sequence “medicine table” using the proposed method M3. Frames 20, 36, 55, 70, 100 and 112 are displayed.

CONCLUSIONS :

We divide the tracking methods into three categories based on the use of object representations, namely, methods establishing point correspondence, methods using primitive geometric models, and methods using contour evolution. Note that all these classes require object detection at some point.

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IORD Journal of Science & Technology E-ISSN: 2348-0831 Volume 1, Issue VI (SEPT-OCT 2014) PP 25-29 IMPACT FACTOR 1.719 www.iord.in

REFERENCES 1

L. Wang, W. Hu, and T. Tan. Recent developments in human motion analysis. Pattern Recognition, 36(3):585–601, March 2003.

R. T. Collins et al. A system for video surveillance and monitoring: VSAM final report. Technical report CMU-RI-TR-00-12, Robotics Institute, Carnegie Mellon University, May 2000.

A. Amer. Voting-based simultaneous tracking of multiple video objects. In Proc. SPIE Int. Symposium on Electronic Imaging, pages 500–511, Santa Clara, USA, January 2003.

I. Haritaoglu, R. Cutler, D. Harwood, and L. S. Davis. Backpack: Detection of people carrying objects using silhouettes. Computer Vision and Image Understanding, 81(3):385–397, 2001.

J. S. Marques, P. M. Jorge, A. J. Abrantes, and J. M. Lemos. Tracking groups of pedestrians in video sequences. In Proc. of IEEE Workshop on Multi-Object Tracking, page 101, Madison, June 2003.

I. Haritaoglu. A Real Time System for Detection and Tracking of People and Recognizing Their Activities. PhD thesis, University of Maryland at College Park, 1998.

D. M. Gavrila. The analysis of human motion and its application for visualsurveillance. In Proc. of the 2nd IEEE International Workshop on Visual Surveillance, pages 3–5, Fort Collins, U.S.A., 1999.

S. Khan and M. Shah. Tracking people in presence of occlusion. In Proc. of Asian Conference on Computer Vision, pages 1132–1137, Taipei, Taiwan, January 2000.

C. Regazzoni and P. Varshney. Multi-sensor surveillance. In IEEE Int. Conf. Image Processing, pages 497–500, 2002.

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