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Overview of Object Detection and Tracking based on Block Matching Techniques

Samdurkar Apurva S., Kamble Shailesh D., Thakur Nileshsingh V., Patharkar Akshay S.

Annals of Computer Science and Information Systems

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2017

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Vol. 10

313--319

EN

Object tracking is one of the vital fields of computer vision that detects the moving object from a video sequence. Object detection is used to detect the object present in the video and to find the exact location of that object. The object tracking can be applied in various fields that include video surveillance, robot vision, traffic monitoring, automated civil or military surveillance system, traffic monitoring, human-computer interaction, vehicle navigation, biomedical image analysis, medical imaging and much more. The object tracking algorithm requires tracking the object in each frame of the video. A common approach is to use the background subtraction, which eliminates the common static background, resulting into foreground region showing the presence of the desired object. Block matching technique is the most popular technique for computing the motion vectors between the two frames of video sequences and different searching techniques are available to compute motion vectors between frames. Still, there is a scope for improvement in modifying or developing a new shape pattern for block matching motion estimation to find out and track the object in the video. This paper presents the several object detection and tracking methods and how block matching can be used to track object from a video.

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Object Motion estimation using edge detection and background subtraction with block matching algorithm

Thapliyal Shardul, Pundir Yogendra Pratap, Bahuguna Arun Shekhar, Semwal Sunil

Annals of Computer Science and Information Systems

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2017

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Vol. 10

345--348

EN

Detection of an object motion is the growing research field of image processing which revealed the several applications. Several techniques (including the proposed one) are discussed so far in literatures. In this paper the edge detection and frame differencing also known as background subtraction technique with block matching algorithm has been implemented to detect the object motion. The object taken for experimentation is arbitrary having no fixed shape and size. The MATLAB output result showing the practicability of the both algorithms.

3

Pomiary przestrzenne w polu obserwacyjnym stereoendoskopu

Goral A.

Przegląd Elektrotechniczny

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2015

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R. 91, nr 5

13-16

PL

W pracy przedstawiono procedurę trójwymiarowej metrycznej rekonstrukcji powierzchni na podstawie zapisu wideo ze stereoendoskopu stosowanego w chirurgii minimalnie inwazyjnej. Metoda bazuje na dopasowaniu obszarami fragmentów obrazów. Wyniki rekonstrukcji porównano z danymi uzyskanymi dla tego samego obiektu metodą referencyjną. Średni błąd rekonstrukcji uzyskany dla poszczególnych klatek sekwencji wynosi od 2,1 do 4,2 mm.

EN

In this work, we present a procedure for 3-dimensional metric surface reconstruction based on video data from a stereoendoscope used in minimally invasive surgery. The reconstruction is based on stereo block matching algorithm. The results of the reconstruction were compared to a reference data set obtained simultaneously for the same object. The mean reconstruction error obtained for individual frames falls within the range of 2.1 to 4.2 mm.

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EBMBDT : Effective Block Matching Based Denoising Technique using Dual Tree Complex Wavelet Transform

Selvi M.

Machine Graphics and Vision

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2014

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Vol. 23, No. 3/4

23--41

EN

In processing and investigation of digital image denoising of images is hence very important. In this paper, we propose a Hybrid denoising technique by using Dual Tree Complex Wavelet Transform (DTCWT) and Block Matching Algorithm (BMA). DTCWT and BMA is a method to identify the noisy pixel information and remove the noise in the image. The noisy image is given as input at first. Then, bring together the comparable image blocks into the load. Afterwards Complex Wavelet Transform (CWT) is applied to each block in the group. The analytic filters are made use of by CWT, i.e. their real and imaginary parts from the Hilbert Transform (HT) pair, defending magnitude-phase representation, shift invariance, and no aliasing. After that, adaptive thresholding is applied to enhance the image in which the denoising result is visually far superior. The proposed method has been compared with our previous denoising technique with Gaussian and salt-pepper noise. From the results, we can conclude that the proposed de-noising technique have shown better values in the performance analysis.

5

Inter-frame Prediction with Fast Weighted Low-rank Matrix Approximation

Huang Z. L., Hsiao H. F.

International Journal of Electronics and Telecommunications

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2013

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Vol. 59, No. 1

9-16

EN

In the field of video coding, inter-frame prediction plays an important role in improving compression efficiency. The improved efficiency is achieved by finding predictors for video blocks such that the residual data can be close to zero as much as possible. For recent video coding standards, motion vectors are required for a decoder to locate the predictors during video reconstruction. Block matching algorithms are usually utilized in the stage of motion estimation to find such motion vectors. For decoder-side motion derivation, proper templates are defined and template matching algorithms are used to produce a predictor for each block such that the overhead of embedding coded motion vectors in bit-stream can be avoided. However, the conventional criteria of either block matching or template matching algorithms may lead to the generation of worse predictors. To enhance coding efficiency, a fast weighted low-rank matrix approximation approach to deriving decoder-side motion vectors for inter frame video coding is proposed in this paper. The proposed method first finds the dominating block candidates and their corresponding importance factors. Then, finding a predictor for each block is treated as a weighted low-rank matrix approximation problem, which is solved by the proposed column-repetition approach. Together with mode decision, the coder can switch to a better mode between the motion compensation by using either block matching or the proposed template matching scheme.

6

Analiza możliwości wykorzystania liniowych przekształceń geometrycznych w kompresji sekwencji wizyjnych

Skurowski P.

Studia Informatica

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2010

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Vol. 31, nr 1

5-18

PL

Standardowe metody kompresji sekwencji wizyjnych bazują m.in. na predykcji obrazu na podstawie wektorów przesunięć. Istnieje możliwość rozszerzenia metody i stworzenia algorytmu rekonstruującego obrazy, używającego wektorów przesunięć, kąta obrotu i współczynnika przeskalowania. Do ich poszukiwania i pasowania bloków zaproponowano użycie transformaty Mellina i Fouriera-Mellina. Funkcjonowanie i zbieżność rozwiązania zweryfikowano względem poszukiwania podobieństwa obrazów na zestawie obrazów testowych.

EN

Standard video compression algorithms are based on motion vector prediction of images. There is potential possibility for enhancement of such an approach and creating an algorithm that predict images using motion vector rotation angle and scale factor as well. As a tool for searching these parameters and block matching there were Mellin and Fourier-Mellin transform proposed. The usability of proposed solution and the convergence of searching was verified against test images.