A featureless and stochastic approach to on-board stereo vision system pose

Dornaika, Fadi; Sappa, Ángel D.

doi:10.1016/j.imavis.2008.12.006

Cited by 11 publications

(9 citation statements)

References 35 publications

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“…That is, the proposed switching algorithm was used to detect a moving object through block-based motions and depth maps extracted from the captured images. Subsequently, the precision rate in (8) and the recall rate in (9) were applied to measure the accuracy of moving object detection on average according to each method:…”

Section: Resultsmentioning

confidence: 99%

“…The stereo camera is one of the most generally used camera types [8]. Such a camera has a separate image sensor and a film frame for each lens, consisting of two or more lenses.…”

Section: Related Workmentioning

confidence: 99%

“…Among them, a camera for taking scenes is very important because it greatly influences the performance of its accompanying image-processing work. Generally, the cameras used most in computer vision and pattern recognition fields are stereo cameras [8], structured-light cameras [9], TOF (time-of-flight) cameras [10], infrared cameras [11], and thermal imaging cameras [12].…”

Section: Introductionmentioning

confidence: 99%

“…The stereo camera [8] uses two lenses to discern an object three-dimensionally, just as human eyes do. The stereo camera is one of the most widely used camera types but is unable to discern an object when illumination is too bright or too dark.…”

Section: Introductionmentioning

confidence: 99%

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A Multiple Feature-Based Image-Switching Strategy in Visual Sensor Networks

Jang

Kim

2015

International Journal of Distributed Sensor Networks

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Generally, one fixed camera is used to take still or dynamic images and extract proper information from the captured images. However, the process of analyzing images through the use of one camera is very sensitive to neighboring environmental factors, such as illumination, background, and noise; thus, it is hard to guarantee precision. To extract proper information from images more precisely in visual sensor networks, this paper proposes an image-switching strategy where, among different types of installed cameras, the one camera best suited to neighboring circumstances is chosen. The proposed strategy is to first receive initial images as input data and then extract multiple features representing neighboring circumstances from the input images. Subsequently, it is to define the neighboring circumstances metric, which is the weighted sum of the extracted features, and to dynamically switch cameras to obtain images in accordance with the neighboring circumstances. The results of the experiment show that the proposed dynamic switching strategy reliably chooses, from among different cameras, the one camera that is best suited to the neighboring circumstances.

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Section: Resultsmentioning

confidence: 99%

“…The stereo camera is one of the most generally used camera types [8]. Such a camera has a separate image sensor and a film frame for each lens, consisting of two or more lenses.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Multiple Feature-Based Image-Switching Strategy in Visual Sensor Networks

Jang

Kim

2015

International Journal of Distributed Sensor Networks

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“…The necessary condition for the calculation of such homography is using only the area, in both images, where the roadway appears. The roadway can be detected by means of a segmentation process based on color, as in [ 34 , 35 ], or using only a region of interest near the vehicle where it is possible to assume that almost the whole region of interest corresponds to the roadway [ 36 ].…”

Section: State Of the Artmentioning

confidence: 99%

Pose Self-Calibration of Stereo Vision Systems for Autonomous Vehicle Applications

Musleh

Martín

Armingol

et al. 2016

Sensors

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Nowadays, intelligent systems applied to vehicles have grown very rapidly; their goal is not only the improvement of safety, but also making autonomous driving possible. Many of these intelligent systems are based on making use of computer vision in order to know the environment and act accordingly. It is of great importance to be able to estimate the pose of the vision system because the measurement matching between the perception system (pixels) and the vehicle environment (meters) depends on the relative position between the perception system and the environment. A new method of camera pose estimation for stereo systems is presented in this paper, whose main contribution regarding the state of the art on the subject is the estimation of the pitch angle without being affected by the roll angle. The validation of the self-calibration method is accomplished by comparing it with relevant methods of camera pose estimation, where a synthetic sequence is used in order to measure the continuous error with a ground truth. This validation is enriched by the experimental results of the method in real traffic environments.

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On-Board Monocular Vision System Pose Estimation through a Dense Optical Flow

Onkarappa¹,

Sappa²

2010

Lecture Notes in Computer Science

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A featureless and stochastic approach to on-board stereo vision system pose

Cited by 11 publications

References 35 publications

A Multiple Feature-Based Image-Switching Strategy in Visual Sensor Networks

A Multiple Feature-Based Image-Switching Strategy in Visual Sensor Networks

Pose Self-Calibration of Stereo Vision Systems for Autonomous Vehicle Applications

On-Board Monocular Vision System Pose Estimation through a Dense Optical Flow

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