Frame-based tracing of multiple objects

Teknomo, Kardi; Takeyama, Yasushi; Inamura, Hajime

doi:10.1109/mot.2001.937976

Cited by 10 publications

(13 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For robust tracking of an object it utilizes features available on the tracked object together with additional parameters obtained from tracking the extended features detected in an image. The additional parameters used in tracking range from velocity of features [2,5,31], path of features [8,9,31], deformable shape of region [1,32,33], region color [29,34] and region sizes [35][36][37].…”

Section: Related Work On Tracking Methodologiesmentioning

confidence: 99%

“…Matsuzawa et al [34] Color Tracking uses neural network for shape representation. Utilizes Mahalanobis Distance with color information for tracking Teknomo et al [36] Color Tracking based on feature such as area, perimeter box width and color. Moritomo et al [37] Color Tracking based on area, width and height of blob, position and color of blob.…”

Section: Related Work On Tracking Methodologiesmentioning

confidence: 99%

“…In addition to the existing tracking methods, color information is also used in tracking [7,29,36,37]. For robust tracking in color, the typical tracking method uses color [3,7,10] and region properties such as color of head region, height of head region, area, width, length, perimeter, shape and position [6,29,33,36,37,47]. Although color information adds robustness to object tracking it is also susceptible to brightness variations [10].…”

Section: Related Work On Tracking Methodologiesmentioning

confidence: 99%

See 2 more Smart Citations

Colour Space Effect on Tracking in Video Surveillance

Sebastian¹,

Voon²,

Comley³

2010

ijeei

View full text Add to dashboard Cite

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.

show abstract

Section: Related Work On Tracking Methodologiesmentioning

confidence: 99%

Section: Related Work On Tracking Methodologiesmentioning

confidence: 99%

Section: Related Work On Tracking Methodologiesmentioning

confidence: 99%

See 1 more Smart Citation

Colour Space Effect on Tracking in Video Surveillance

Sebastian¹,

Voon²,

Comley³

2010

ijeei

View full text Add to dashboard Cite

show abstract

“…To date, feature tracking has been extended to a number of applications including video surveillance [13][14][15], medical research [16,17], image reconstruction [18] and orientation [19,20], trajectory measurement [21,22] and autonomous road-lighting performance assessment [23]. This paper proposes a fast, robust and autonomous feature-matching technique that is capable of tracking sequences of airport landing lighting data during a normal descent to an airport.…”

Section: Autonomous Feature Trackingmentioning

confidence: 99%

Fast model-based feature matching technique applied to airport lighting

Niblock

Jia

McMenemy

et al. 2008

IET Science, Measurement &Amp; Technology

View full text Add to dashboard Cite

Irwin, G. (2008Abstract: The use of image processing techniques to assess the performance of airport landing lighting using images of it collected from an aircraft-mounted camera is documented. In order to assess the performance of the lighting, it is necessary to uniquely identify each luminaire within an image and then track the luminaires through the entire sequence and store the relevant information for each luminaire, that is, the total number of pixels that each luminaire covers and the total grey level of these pixels. This pixel grey level can then be used for performance assessment. The authors propose a robust model-based (MB) featurematching technique by which the performance is assessed. The development of this matching technique is the key to the automated performance assessment of airport lighting.The MB matching technique utilises projective geometry in addition to accurate template of the 3D model of a landing-lighting system. The template is projected onto the image data and an optimum match found, using nonlinear least-squares optimisation. The MB matching software is compared with standard feature extraction and tracking techniques known within the community, these being the Kanade -Lucus -Tomasi (KLT) and scaleinvariant feature transform (SIFT) techniques. The new MB matching technique compares favourably with the SIFT and KLT feature-tracking alternatives. As such, it provides a solid foundation to achieve the central aim of this research which is to automatically assess the performance of airport lighting.

show abstract

“…Tracking objects in an image sequence has been discussed in many papers [4] [5] [6]. The method we used to track objects is similar as in [7].…”

Section: Capture the Moving Objects And Define The Spatial Relationshipsmentioning

confidence: 99%

An Application Based on Spatial-Relationship to Basketball Defensive Strategies

Chin

Huang

Tang

et al. 2005

Embedded and Ubiquitous Computing – EUC 2005 Workshops

View full text Add to dashboard Cite

Abstract. This paper aims to develop a simulated system used for teaching and training basketball defensive strategies. Respectively, defensive strategies can be described within one method by editing video recorded from basketball games into desired clips for analysis and storing them into the database. In this paper, we used Spatial-Temporal Relationships to describe the local defensive movements by the basketball players in a game. The system will automatically capture tracks of defensive movements by the basketball players in the video clips, from which basketball coaches and players can learn various defensive strategies within the shortest period of time. The simulated system is expected to become a computerized educational aid to basketball teaching and training and to replace the unscientific and stereotyped system of basketball teaching and training.

show abstract

Frame-based tracing of multiple objects

Cited by 10 publications

References 12 publications

Colour Space Effect on Tracking in Video Surveillance

Colour Space Effect on Tracking in Video Surveillance

Fast model-based feature matching technique applied to airport lighting

An Application Based on Spatial-Relationship to Basketball Defensive Strategies

Contact Info

Product

Resources

About