A fast template matching algorithm with adaptive skipping using inner-subtemplates' distances

Kawanishi, T.; Kurozumi, T.; Kashino, K.; Takagi, S.

doi:10.1109/icpr.2004.1334614

Cited by 24 publications

(8 citation statements)

References 3 publications

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“…Yapılan literatür araştırmasında Farkların Mutlak Değerlerinin Toplamı Algoritmasını kullanarak yapılan Tüm Çerçeve Taramasını hızlandırmak için önerilen yöntemler [5][6][7] olmakla beraber böyle bir yöntem daha önce önerilmemiştir.…”

Section: öNerilen İki̇ Aşamali Yöntemunclassified

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A two stage template matching algorithm and its İmplementation on FPGA

Aktaş

Sever

Töreyi̇n

2015

2015 23nd Signal Processing and Communications Applications Conference (SIU)

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Özetçe -Bu bildiride işlem yoğunluğu yüksek Şablon Eşleştirme Algoritması için tur sayısını ve işlem yükünü azaltacak iki aşamalı yeni bir algoritma önerilmiştir. Eşleştirme için Farkların Mutlak Değerlerinin Toplamı metodu kullanılmıştır. Geliştirilen bu algoritma, Alan Programlanabilir Kapı Dizisi (FPGA) üzerinde gerçeklenmiştir. Algoritma, FPGA üzerindeki rastgele erişimli bellek öbekleri (Block RAMs) efektif bir şekilde kullanılarak hızlandırılmıştır. Bu sayede, geliştirilen algoritma, İnsanız Hava Aracı (İHA -UAV) görüntülerinde gerçek zamanlı nesne takibi yapabilecek hâle getirilmiştir. Abstract-In this paper, to decrease the computational cost and number of cycles in Template Matching Algorithm, a novel two-stage algorithm is proposed. The Sum of Absolute Differences method is used for matching. The proposed algorithm is implemented on Field-Programmable-Gate-Array (FPGA). The algorithm is accelerated with the effective usage of Block RAMs distributed on FPGA. Thus, the proposed algorithm became fast enough for real time object tracking applications on UAVs.

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Section: öNerilen İki̇ Aşamali Yöntemunclassified

“…Bu işlemde doğruluk oranı çok yüksek olmasına karşın, işlem yükü oldukça fazladır. Şablon eşleştirme algoritmalarını hızlandırmak için geliştirilmiş birçok yöntem bulunmaktadır [5][6][7].…”

Section: Introductionunclassified

A two stage template matching algorithm and its İmplementation on FPGA

Aktaş

Sever

Töreyi̇n

2015

2015 23nd Signal Processing and Communications Applications Conference (SIU)

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“…The GRAPH-TRACK search algorithm is able to handle a few hundred candidates per frame efficiently, as opposed to the four candidates per frame used by DP-TRACK. Finding the candidates is essentially a template matching problem, and could be addressed by methods such as [3,11,8,5], but for offline tracking it is allowable to invest some time into the preprocessing, if this leads to immediate feedback during the user interaction. Following [4] we therefore preprocess the image such that each patch is represented by a feature vector.…”

Section: Candidate Selectionmentioning

confidence: 99%

GraphTrack: Fast and globally optimal tracking in videos

Amberg

Vetter

2011

CVPR 2011

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“…One of the simplest methods of object detection is usage of distance between pixels to the reference image pixels [18]. In this method, the slightest change in appearance such as size and speed has adversely affected on the detection results.…”

Section: Scene Analysis Systemmentioning

confidence: 99%

Dynamic and robust method for detection and locating vehicles in the video images sequences with use of image processing algorithm

Farahani

2017

J Image Video Proc.

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There are various methods in the field of moving-object tracking in the video images that each of them implies on the specific features of object. Among tracking methods based on features, algorithms based on color are able to provide a precise description of the object and track the object with high speed. One of the efficient methods in the field of object tracking based on color information is mean-shift algorithm. If the color of moving object approaches toward a background model or image background has a low contrast and brightness, then the color information is not enough for target tracking. In this paper, the new tracking method is proposed which with combination of moved object information with color information, the new proposed method will be capable to track object under condition that color information is not enough for tracking. With use of background subtraction method based on Gaussian combination, the binary image which includes moving information will use in the mean-shift algorithm. Usage of object movement information will compensate the lack of spatial information and will increase robustness of algorithm especially in the complicated conditions. Also in order to achieve the robust algorithm against changes in shapes, size, and rotation of object, extended mean-shift algorithm is used. Results show the robustness of proposed algorithm in object tracking especially under conditions which object color is same as background color and have better results in the low contrast condition in comparison to mean-shift and extended mean-shift algorithms.

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A fast template matching algorithm with adaptive skipping using inner-subtemplates' distances

Cited by 24 publications

References 3 publications

A two stage template matching algorithm and its İmplementation on FPGA

A two stage template matching algorithm and its İmplementation on FPGA

GraphTrack: Fast and globally optimal tracking in videos

Dynamic and robust method for detection and locating vehicles in the video images sequences with use of image processing algorithm

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A fast template matching algorithm with adaptive skipping using inner-subtemplates' distances

Cited by 24 publications

References 3 publications

A two stage template matching algorithm and its &#x0130;mplementation on FPGA

A two stage template matching algorithm and its &#x0130;mplementation on FPGA

GraphTrack: Fast and globally optimal tracking in videos

Dynamic and robust method for detection and locating vehicles in the video images sequences with use of image processing algorithm

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Product

Resources

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A two stage template matching algorithm and its İmplementation on FPGA

A two stage template matching algorithm and its İmplementation on FPGA