A coded visual marker for video tracking system based on structured image analysis

Kawano, T.; Ban, Y.; Uehara, K.

doi:10.1109/ismar.2003.1240711

Cited by 12 publications

(13 citation statements)

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“…The camera pose can then be estimated from these corresponding points [23][24][25]. Drummond et al [24] found camera poses that correctly re-project some fixed features of a prior 3D model into the 2D image.…”

Section: Related Work About 3d Pose Estimation From a Planar Targetmentioning

confidence: 99%

“…Some researchers have established a relationship between images and the real world using fiducial markers. For instances, Kawano et al [23] discussed a number of planar markers for Augmented Reality (AR). The salient markers with known pose information in the real world were easily detected in the images.…”

Section: Related Work About 3d Pose Estimation From a Planar Targetmentioning

confidence: 99%

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Combining Front Vehicle Detection with 3D Pose Estimation for a Better Driver Assistance

Peng

et al. 2012

International Journal of Advanced Robotic Systems

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Driver assistant systems enhance traffic safety and efficiency. The accurate 3D pose of a front vehicle can help a driver to make the right decision on the road. We propose a novel real-time system to estimate the 3D pose of the front vehicle. This system consists of two parallel threads: vehicle rear tracking and mapping. The vehicle rear is first identified in the video captured by an onboard camera, after license plate localization and foreground extraction. The 3D pose estimation technique is then employed with respect to the extracted vehicle rear. Most current 3D pose estimation techniques need prior models or a stereo initialization with user cooperation. It is extremely difficult to obtain prior models due to the varying appearance of vehicles' rears. Moreover, it is unsafe to ask for drivers' cooperation when a vehicle is running. In our system, two initial keyframes for stereo algorithms are automatically extracted by vehicle rear detection and tracking. Map points are defined as a collection of point features extracted from the vehicle's rear with their 3D information. These map points are inferences that relate the 2D features detected in following vehicles' rears with the 3D world. The relative 3D pose of the onboard camera to the front vehicle rear is then estimated through matching the map points with point features detected on the front vehicle rear. We demonstrate the capabilities of our system by testing on real-time and synthesized videos. In order to make the experimental analysis visible, we demonstrated an estimated 3D pose through augmented reality, which needs accurate and real-time 3D pose estimation.Keywords 3D pose estimation, vehicle rear tracking, driver assistance system Brief Abstract for Correspondences Driver assistant systems enhance traffic safety and efficiency. The accurate 3D pose of a front vehicle can help a driver to make the right decision on the road. We originally propose a real-time system to estimate the 3D pose of the front vehicle. This system consists of two parallel threads: vehicle rear tracking and mapping. The vehicle rear is first identified in the video captured by an onboard camera, after license plate localization and foreground extraction. The 3D pose estimation technique is then employed with respect to the extracted vehicle rear. We demonstrate the capabilities of our system by testing on real-time and synthesized videos. In order to make the experimental analysis visible, we demonstrated an estimated 3D pose through augmented reality, which needs accurate and real-time 3D pose estimation.

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Section: Related Work About 3d Pose Estimation From a Planar Targetmentioning

confidence: 99%

Section: Related Work About 3d Pose Estimation From a Planar Targetmentioning

confidence: 99%

Combining Front Vehicle Detection with 3D Pose Estimation for a Better Driver Assistance

Peng

et al. 2012

International Journal of Advanced Robotic Systems

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“…However, we cannot know what ingredient the recognized object is because we cannot extract ingredient's features from the infrared camera. To solve this problem, we use the marker proposed in [5]. Fig.…”

Section: Recognition Of Objects and Actions In Cookingmentioning

confidence: 99%

The Interactive Cooking Support System in Mixed Reality Environment

Horie

Mega

Uehara

2006

2006 IEEE International Conference on Multimedia and Expo

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Recently, many learning systems, such as e-learning and WBT (Web Based Teaching) systems have been developed. In these systems, users can get educational contents and graphical material in a remote place through the network. However, the learning is always one-sided. That is, all users learn the same contents and cannot convey their states like "What is he or she doing now?" to these systems. Therefore, these systems are not necessarily satisfying all the users' demands.In this paper, we propose an interactive learning application of cooking in mixed reality (MR) environment. In an MR environment, a system should not only provide the useful knowledge, but also recognize users' state. Furthermore, by making a parallel transition model about cooking, it is possible to control the user's cooking process. Therefore, users can learn details about cooking more flexibly and effectively.

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“…Visual Markers derived from barcodes are usually employed [5,6,7,8,9], but also Infra-Red (IR) or Radio Frequency (RF) tags have been proposed [3,4]. A tagging technology turns out to be more suitable than another depending on the characteristics of the tagged objects and of the global environment.…”

Section: Introductionmentioning

confidence: 99%

“…The traditional Visual Tag detection algorithms are based on gray-scale images and use black-and-white tags [7,8,9]. Their reliability in detection and decoding depends often on the illumination conditions.…”

Section: Introductionmentioning

confidence: 99%

Colored visual tags: a robust approach for augmented reality

Dell’Acqua

Ferrari²,

Marcon³

et al.

Proceedings. IEEE Conference on Advanced Video and Signal Based Surveillance, 2005.

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This paper presents a robust method for fast Visual Tags reading, suitable for Augmented Reality (AR) environments. Tag detection is based on well known tools of imageprocessing, but their combination, together with the use of colored markers, allows a robust recognition even with lowcost CMOS or CCD cameras and in poorly illuminated environments. In particular the color mix and the structure of the tag are quite unusual in common environments and can be easily detected with color filtering and geometric analysis. The proposed tag carries binary information encoded in its structure: in the presented implementation a 32-bit code with 12 parity bits is encoded in the tag but extensions to longer codes can be easily devised.

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A coded visual marker for video tracking system based on structured image analysis

Cited by 12 publications

References 1 publication

Combining Front Vehicle Detection with 3D Pose Estimation for a Better Driver Assistance

Combining Front Vehicle Detection with 3D Pose Estimation for a Better Driver Assistance

The Interactive Cooking Support System in Mixed Reality Environment

Colored visual tags: a robust approach for augmented reality

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