Viewing distance measurement using a single camera

Pang, Yu; Zhao, Yan; Chen, Jian; Wang, Shigang; Chen, Hexin

doi:10.1109/itaic.2014.7065103

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…4, December 2023: 325-331 326 but not computationally intensive because it requires only a few image registrations that enable the computer to process the images faster. Thus, this type of method can effectively reduce the system workload and save the computer a longer processing time [11]. The monocular method used for visual servoing purposes is cheap and has low handling complexity due to the use of only one camera.…”

Section: Introductionmentioning

confidence: 99%

Object distance estimation using a monovision camera

Idohen,

Afolayan,

Umar

2023

IJRA

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In computer vision, most monovision cameras used for estimating the position of an object only estimate the 2D information of the object without the depth information. Estimating the depth information, which is the distance between the target object and the camera is quite challenging but, in this paper, a less computationally intensive method was used to estimate the object’s distance to complete the 3D information needed to determine the object’s location in cartesian space. In this method, the object was positioned in front of the camera at a sequential distance and was measured directly. The distances measured in the experiment with a set of training data obtained from the image were fitted into a curve using the least-square framework to derive a non-linear function that was used for estimating the object’s distance also known as the z-coordinate. The result from the experiment showed that there was an average error of 1.33 mm between the actual distance and the estimated distance of the object. Hence, this method can be applied in many robotic and autonomous systems applications.

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Section: Introductionmentioning

confidence: 99%

Object distance estimation using a monovision camera

Idohen,

Afolayan,

Umar

2023

IJRA

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“…They used the variation in eye distance (in pixels) with changes in person-to-camera distance (in inches) to formulate the measuring system. Similarly, Peng et al 26 measured the viewing distance by determining the pixel distance between binocular pupils in the image and fitting a function between the actual distance and the pixel distance of binocular pupil. Kumar et al 27 proposed a methodology for estimating the face distance from a front camera with a back propagation neural network (BPNN), and facial features of a standard model at different depths were extracted to train the BPNN.…”

Section: Introductionmentioning

confidence: 99%

Recognition and distance estimation of an irregular object in package sorting line based on monocular vision

Wang

Zhou

et al. 2019

International Journal of Advanced Robotic Systems

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In this article, we propose a monocular vision-based approach that can simultaneously recognize an object and estimate the distance to the target in package classification. Calibration is necessary due to lack of depth information in a single RGB image, and template matching makes it possible to estimate the distance of an irregular object without measurable parameters. First of all, capture images of the particular object as templates at set distances. Then, simplify the feature extraction to abandon the scale invariance. By exploiting a nonparametric estimation, the relationship between local feature correspondence and the similarity of two images is theoretically explored. Finally, the object will be recognized and the scale grade of it will be determined at the same time based on two-stage template matching. Experimental results have proved the high accuracy of our approach that has then been successfully applied to a real-time automatic package sorting line.

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