Tracking using normalized cross correlation and color space

Sebastian, Patrick; Voon, Yap Vooi

doi:10.1109/icias.2007.4658490

Cited by 14 publications

(15 citation statements)

References 4 publications

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“…m n P P = − V (9) Finally, the angle of the vector which determines the direction of the object's motion is calculated using the Equation (10).…”

Section: Motion Estimation Stagementioning

confidence: 99%

“…Cross-correlation [9], on the other hand, was used to implement a face tracking algorithm for video conferencing environment. This method compares a region of the image with a known signal extracted from the object of interest, and then a measure of similarity is used to determine the exact position of the object being tracked in the next frame.…”

Section: Introductionmentioning

confidence: 99%

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Rebound of Region of Interest (RROI), a New Kernel-Based Algorithm for Video Object Tracking Applications

Ramirez¹,

Chouikha²

2014

JSIP

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This paper presents a new kernel-based algorithm for video object tracking called rebound of region of interest (RROI). The novel algorithm uses a rectangle-shaped section as region of interest (ROI) to represent and track specific objects in videos. The proposed algorithm is constituted by two stages. The first stage seeks to determine the direction of the object's motion by analyzing the changing regions around the object being tracked between two consecutive frames. Once the direction of the object's motion has been predicted, it is initialized an iterative process that seeks to minimize a function of dissimilarity in order to find the location of the object being tracked in the next frame. The main advantage of the proposed algorithm is that, unlike existing kernel-based methods, it is immune to highly cluttered conditions. The results obtained by the proposed algorithm show that the tracking process was successfully carried out for a set of color videos with different challenging conditions such as occlusion, illumination changes, cluttered conditions, and object scale changes.

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“…m n P P = − V (9) Finally, the angle of the vector which determines the direction of the object's motion is calculated using the Equation (10).…”

Section: Motion Estimation Stagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rebound of Region of Interest (RROI), a New Kernel-Based Algorithm for Video Object Tracking Applications

Ramirez¹,

Chouikha²

2014

JSIP

View full text Add to dashboard Cite

show abstract

“…Cross-correlation, on the other hand, was used in [9] to implement a face tracking algorithm for video conferencing environment. This method compares a region of the image with a known signal extracted from the object of interest, and then a measure of similarity, which allows to determine the exact position of the object being tracked in the next frame, is obtained between the two signals.…”

Section: Related Workmentioning

confidence: 99%

“…The Equation (9) presents the function of dissimilarity, M(∆x, ∆y), which is used to compare these two regions. (9) where the parameter, L, represents the number of pixels which constitutes the region of interest , R 1 . The function, U(.…”

Section: Estimation Of the Direction Of The Object's Motionmentioning

confidence: 99%

A New Algorithm for Tracking Objects in Videos of Cluttered Scenes

Ramirez¹,

Chouikha²

2013

IJITMC

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The work presented in this paper describes a novel algorithm for automatic video object tracking based on a process of subtraction of successive frames, where the prediction of the direction of movement of the object being tracked is carried out by analyzing the changing areas generated as result of the object's motion, specifically in regions of interest defined inside the object being tracked in both the current and the next frame. Simultaneously, it is initiated a minimization process which seeks to determine the location of the object being tracked in the next frame using a function which measures the grade of dissimilarity between the region of interest defined inside the object being tracked in the current frame and a moving region in a next frame. This moving region is displaced in the direction of the object's motion predicted on the process of subtraction of successive frames. Finally, the location of the moving region of interest in the next frame that minimizes the proposed function of dissimilarity corresponds to the predicted location of the object being tracked in the next frame. On the other hand, it is also designed a testing platform which is

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“…The color spaces of YCbCr and HSV can be used for more accurate tracking due to these color spaces having less noise [19] and the ability of using different layers without losing data [21,44] and tracking ability [19]. [53] Grayscale Normalized cross correlation Nillius et al [54] Wavelet Normalized cross correlation McErlean [55] Wavelet Phase correlation Atsushin et al [5] Grayscale Multiple cameras tracking on binary image of human blob. Tracking of target is based on ellipsoid shape and velocity of blob.…”

Section: Related Work On Tracking Methodologiesmentioning

confidence: 99%

Colour Space Effect on Tracking in Video Surveillance

Sebastian¹,

Voon²,

Comley³

2010

ijeei

Self Cite

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Abstract:The utilization of video surveillance systems is becoming common and is expected to more widespread as societies become more complex and the population continues to grow. The implementation of these video surveillance systems has provided valuable information and assistance in monitoring large areas. These systems typically use human operators to determine human behavior and to manually track people or objects of interest over an array of cameras. With the application of computers with video surveillance, real time surveillance of large public areas, people and their activities has been made possible for monitoring and security. The main contribution of this paper is to show the effect of color space on tracking methods in video surveillance. Results from evaluations on different tracking methods have indicated that YCbCr and HSV color spaces have better tracking ability compared to grayscale and RGB color spaces. In addition, the results from evaluations have also indicated that data from selected layers in some color spaces can be used for the purpose of tracking namely the Cb and Cr layers from the YCbCr color space and the H layer from the HSV color space.Keywords: Video, Tracking, Video Surveillance, Tracking Metrics, Color space IntroductionThe utilization of video surveillance systems is becoming common and is expected to more widespread as societies become more complex and the population continues to grow. In utilizing video surveillance in a large scale environment, it leads to problems in recognizing human behavior and tracking people over an array of cameras. The task of recognizing human behavior is difficult by itself and in adding the task of tracking a particular person or persons over an array of cameras makes the task even more difficult for a human operator.The motivation of this paper is to show how different color spaces effects the tracking performance of a video surveillance system. The paper is divided into 6 sections. Section 2 covers the fundamental background information related to colour spaces of images. Section 3 covers recent and related research work in tracking methodologies. Section 4 covers the experimental setup for evaluating the effect on tracking performance on different colour spaces. Section 5 covers the results of the experiments done in determining tracking performance in different colour spaces.

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Tracking using normalized cross correlation and color space

Cited by 14 publications

References 4 publications

Rebound of Region of Interest (RROI), a New Kernel-Based Algorithm for Video Object Tracking Applications

Rebound of Region of Interest (RROI), a New Kernel-Based Algorithm for Video Object Tracking Applications

A New Algorithm for Tracking Objects in Videos of Cluttered Scenes

Colour Space Effect on Tracking in Video Surveillance

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