A model-based 3-D tracking of rigid objects from a sequence of multiple perspective views

Jung, Soon Ki; Wohn, Kwang Yun

doi:10.1016/s0167-8655(98)00023-3

Cited by 10 publications

(5 citation statements)

References 16 publications

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“…Much research has been conducted in the field of computer science to develop efficient tracking methods. Previous studies on object tracking can be broadly divided into two categories: feature‐based and model‐based object tracking methods (Zhang and Faugeras, 1992; Jung and Wohn, 1998; Marchand et al, 2001). The feature‐based approach generates estimates of feature positions in sequential image frames to analyze the real motion of objects (Zhang and Faugeras, 1992).…”

Section: Background and Literature Reviewmentioning

confidence: 99%

“…However, the feature‐based approach does have limitations. When the motion of an object is complex—for example, if an object is randomly and quickly moving in an obstacle‐cluttered environment—it is difficult to obtain a reliable feature from the image as some parts of the image can be invisible or distorted (Jung and Wohn, 1998). In such a case, finding resemblances between two extracted features could be meaningless.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

“…The feature-based tracking algorithm has advantages stemming from its simple motion tracking capability; a high-level knowledge of an object's geometry is not required as this approach assumes that each extracted feature in a frame is independent from any other (Jung and Wohn, 1998). However, the feature-based approach does have limitations.…”

Section: Object Tracking Algorithmsmentioning

confidence: 99%

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A Methodology for Object Identification and Tracking in Construction Based on Spatial Modeling and Image Matching Techniques

Chi

Caldas

Kim

2009

Computer aided Civil Eng

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Understanding the motion characteristics of on-site objects is desirable for the analysis of construction work zones, especially in problems related to safety and productivity studies. This article presents a methodology for rapid object identification and tracking. The proposed methodology contains algorithms for spatial modeling and image matching. A high-frame-rate range sensor was utilized for spatial data acquisition. The experimental results indicated that an occupancy grid spatial modeling algorithm could quickly build a suitable work zone model from the acquired data. The results also showed that an image matching algorithm is able to find the most similar object from a model database and from spatial models obtained from previous scans. It is then possible to use the matched information to successfully identify and track objects.

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Section: Background and Literature Reviewmentioning

confidence: 99%

Section: Background and Literature Reviewmentioning

confidence: 99%

Section: Object Tracking Algorithmsmentioning

confidence: 99%

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A Methodology for Object Identification and Tracking in Construction Based on Spatial Modeling and Image Matching Techniques

Chi

Caldas

Kim

2009

Computer aided Civil Eng

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“…At present, various methods of target tracking and detection have been put forward in many literatures. In a word, targets are detected through imaging system and the relevant data are obtained by using the method of image processing or radar tracking [3].…”

Section: Introductionmentioning

confidence: 99%

Image Processing and Recognition Algorithm for Target Tracking

Lü

Wang

2016

International Journal on Smart Sensing and Intelligent Systems

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Abstract-to improve target tracking performance in dynamic target track system, this paper propose the processing method of positive and negative difference image to extract target information; research target image preprocessing algorithm, the separation and segmentation processing algorithm of target and background, target edge detection and extraction based on the collected images; use Laplace operator, Canny operator. Gauss-Laplace operator to gain target information and improved recognition target image effect, analyze the positive and negative difference image measure to solve the background subtraction interference between two sequence images. Through the actual image processing, The results shows the proposed processing method can clear gain target marginal information and target image particle center, control tracking platform can stably track the target, and give the comparison results.

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“…In addition, occlusion phenomena are more difficult to deal with. Further details can be found in [14].…”

Section: Introductionmentioning

confidence: 99%

Correlation-based particle filter for 3D object tracking

Noyer

Lanvin

Benjelloun

2009

ICA

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This manuscript deals with the problem of 3D object tracking in a multisensor framework. The object is here described by a CAD model. It avoids any image preprocessing that leads, generally, to loss of information. We develop a particle filtering method [6] that we call "correlation-based particle filter" (CBPF) to solve this non-linear estimation problem. The new proposed approach is applied to synthetic and real image sequences of complex 3D moving objects. The originality of this work consists of developing a centralized fusion method that uses, in an optimal way, the measurements delivered by the sensors. In order to optimally using the sensor outcomes, a centralized fusion approach is proposed. The method can jointly estimate 3D pose/motion parameters and track the object in the 3D domain, while many works have been developed in the image plane. Finally, we should mention that the method is not limited in terms of object structure and motion.

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A model-based 3-D tracking of rigid objects from a sequence of multiple perspective views

Cited by 10 publications

References 16 publications

A Methodology for Object Identification and Tracking in Construction Based on Spatial Modeling and Image Matching Techniques

A Methodology for Object Identification and Tracking in Construction Based on Spatial Modeling and Image Matching Techniques

Image Processing and Recognition Algorithm for Target Tracking

Correlation-based particle filter for 3D object tracking

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