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Article
Proposed Image Similarity Metric with Multi Block Histogram used in Video Tracking

Authors: Alia K. Abdul Hassan --- Hasanen S. Abdullah --- Akbas E. Ali
Journal: Engineering and Technology Journal مجلة الهندسة والتكنولوجيا ISSN: 16816900 24120758 Year: 2016 Volume: 34 Issue: 4 Part (B) Scientific Pages: 578-584
Publisher: University of Technology الجامعة التكنولوجية

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Abstract

One of the important requirements in the object detection and tracking is the extracting of efficient features to trackthe target in video sequence. The feature of colour in image is one of the most visual features widely used. The using ofcolour histogram is the most popular method for representing color feature. One of the problems of using colour histogram to represent feature is its lack of spatial information where it is used to represents statistical distribution of the coloursonly. In this paper a new similarity metric with multi block colour histogram of image is proposed. This metric will be used by an object tracking method where the similarity will be applied to get a decision of choosing the correct solution (location) of the object from many candidate locations


Article
Selection, Detection, and Tracking of Video objects Based on FPGA

Authors: Zaki Y. Abid (BSc)1 --- Thamir R. Saeed2 --- Sameir A. Aziez3
Journal: IRAQI JOURNAL OF COMPUTERS,COMMUNICATION AND CONTROL & SYSTEMS ENGINEERING المجلة العراقية لهندسة الحاسبات والاتصالات والسيطرة والنظم ISSN: 18119212 Year: 2015 Volume: 15 Issue: 1 Pages: 1-17
Publisher: University of Technology الجامعة التكنولوجية

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Abstract

Abstract – This paper presents a moving object tracker for monitoring system which can be used in a smart city. Kernel density estimation (KDE) algorithm has been used for representing a background model, while a minimum distance between the current image and the background has been used to extract the foreground. Also, morphological operations are carried out to remove the noise regions and to filter out ambiguous areas. The performance has been evaluated by determining the true, false, and miss detections of an object area. The optimal results have been obtained by adjusting the morphological operation sequence to be (close > thicken) combination by which the true-hits are 14 out of 16 while miss-number is 2 and zero false-hits, While, the percentage hit ratio was 87.5% (14 out of 16). Also, the salt noise introduction in video reduces the hit number from 14 to 11 when it increases from zero to 0.5 percent of the total frame pixels. The accepted absolute error ratio (in morphological properties of the matched object) is kept at 0.05 for all tests. The implementation has been built by using a combination of two platforms, ISE 14.6(2013) and Matlab(2013a) platforms, to avoid the size weakness of XC3S700A-FPGA board.


Article
Object Tracking using Generalized Gradient Vector Flow

Author: Israa A. Alwan
Journal: Engineering and Technology Journal مجلة الهندسة والتكنولوجيا ISSN: 16816900 24120758 Year: 2011 Volume: 29 Issue: 7 Pages: 1408-1424
Publisher: University of Technology الجامعة التكنولوجية

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Abstract

The aim of an object tracker is to generate the trajectory of an object over timeby locating its position in every frame of the video. In this research, we present anobject contour tracking approach using Generalized Gradient Vector Flow(GGVF). GGVF active contour, or snake, is a dynamic curve that moves within animage domain to capture desired image features. Mostly, GGVF is not sensitive toinitial conditions and converges to the optimal contour. Given an initial contournear the object in the first video frame, GGVF can iteratively converge to anoptimal object boundary. In each video frame thereafter, the resulting contour inthe previous video frame is taken as initialization so the algorithm consists of twosteps. In the first step, the initial contour is applied to the desired object in firstvideo frame. The resulting contour is taken as initialization of the second step,which applies GGVF to current video frame. To evaluate the tracking performance,we applied the algorithm to several real world video sequences. Experimentalresults are provided.


Article
Object Tracking using Proposed Framework of KalmanGuided Harmony Search Filter
تتبع جسم باستخدام اطار مقترحلمرشح كالمان كموجه لمرشح الهارموني

Author: Akbas E. Ali* Dr. Alia K. Abdul Hassan* Dr. Hasanen S. Abdullah*
Journal: AL-yarmouk Journall مجلة كلية اليرموك الجامعة ISSN: 20752954 Year: 2015 Issue: 1 Pages: 120-137
Publisher: College Yarmouk University كلية اليرموك الجامعة

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Abstract

In this paper an improvementon the harmony filter is done by adding the Kalman filter after the improvisation process, toguide the filter to reach the convergence state at the lowest possible number of iterations, which means more ability for tracking the moving objects in real time performance, which is a crucial factor in the multi object tracking applications.

في هذا البحث تم تطوير فلتر البحث الهارموني وذلك باضافة فلتر كالمان اليه بعد عملية الارتجال لغرض توجيه فلتر الهارموني للوصول الى حالة التقارب باقل عدد ممكن من التكرارات،وهومايعني المزيدمن القدرةعلى تتبع الأجسام المتحركة في الأداءفي الوقت الحقيقي،والذي يعتبر عامل حاسم في تطبيقات تتبع الكائنات المتعددة


Article
Tracking of Video Objects Based on Kalman Filter

Authors: Assel H. Kaittan --- Thamir R. Saeed
Journal: Journal of University of Babylon مجلة جامعة بابل ISSN: 19920652 23128135 Year: 2017 Volume: 25 Issue: 5 Pages: 1507-1518
Publisher: Babylon University جامعة بابل

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Abstract

Object tracking is an important task within the field of computer vision. Where, it is the process of segmenting an object of interest from a video scene and keeping track of its motion, orientation occlusion to extract useful information. This paper is intended to the improve the measurement accuracy of the closing moving object by using the median filter for denoising and Kalman filter for tracking. At the denoising stage, 70-80% of the noise is reduced by using a median filter. The median filter has been used instead of the Wiener filter because the noise which is assumed is salt and pepper, and it is less complexity than the Wiener filter. While in the tracking stage, the KF has been used as estimated filter. However, the measurements have been improved by 11.27%. The simulation has been done by using Matlab 2014, while the proposal is applied to the real video.

تتبع الاجسام مطلب مهم مجال رؤية الكمبيوتر. حيث، هو عملية تجزئة الجسم من مشهد فيديو وابقاء التتبع لحركته، لاستخراج المعلومات المفيدة. وتهدف هذه الورقة إلى تحسين دقة القياس من للاجسام المتحركة المتقاربة باستخدام المرشح الوسيط للتصفية ومرشح كالمان للتتبع. في مرحلة إزالة الضوضاء، تم تقليل 70-80٪ من الضوضاء باستخدام مرشح وسيط. وقد استخدم المرشح الوسيط بدلا من مرشح وينر لأن الضوضاء المفترضة هي الملح والفلفل، وهو أقل تعقيدا من مرشح وينر. بينما في مرحلة التتبع، تم استخدام مرشح كالمان كمقدر (مخمن). ومع ذلك، تم تحسين القياسات بنسبة 11.27٪. وقد تم إجراء المحاكاة باستخدام ماتلاب 2014، في حين يتم تطبيق الاقتراح على الفيديو الحقيقي.

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