Robust pedestrian height estimation using principal component analysis and its application to automatic camera calibration

Cho, Woon; Shin, Minwoo; Jang, Jinbeum; Paik, Joonki

doi:10.23919/elinfocom.2018.8330602

Cited by 2 publications

(4 citation statements)

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“…Length estimation methods based on color video are classified into length estimations by camera parameters [2][3][4][5][6], by vanishing points [7][8][9][10][11][12], by prior statistical knowledge [28,29], by gaits [30,31] and by neural networks [32,33]. The length estimation methods by the camera parameters generate an image projection model into an color image by the focal length, the height and the poses of a camera.…”

Section: Object Length Measurement From Color or Depth Informationmentioning

confidence: 99%

“…In order to overcome this disadvantage, Liu [2] introduces an estimation method for the camera parameters using prior knowledge about the distribution of relative human heights. Cho [6] proposes an estimation method for the camera parameters by tracking the poses of human body from a sequence of frames. The length estimation methods by the vanishing points use a principle that several parallel lines in 3D space meet at one point in a 2D image.…”

Section: Object Length Measurement From Color or Depth Informationmentioning

confidence: 99%

“…If σ 2 r is smaller than T σ , then the d h is corrected as the depth value of the corresponding pixel as shown in Figure 7. Otherwise, the point is found between pixels below one pixel and the similarity of the found point is calculated by (6). In (6), r is smaller as d h is larger because the width of an object is larger as the distance from the camera is closer as follows [42]:…”

Section: Human Height Estimationmentioning

confidence: 99%

“…Various studies have been conducted to estimate human height from color video. Human height is estimated by obtaining 3D information of the human body from color video [2][3][4][5][6]. Both the position and the pose of the camera are required in order to obtain 3D information of human body.…”

Section: Introductionmentioning

confidence: 99%

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Human Height Estimation by Color Deep Learning and Depth 3D Conversion

et al. 2020

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In this study, an estimation method for human height is proposed using color and depth information. Color images are used for deep learning by mask R-CNN to detect a human body and a human head separately. If color images are not available for extracting the human body region due to low light environment, then the human body region is extracted by comparing between current frame in depth video and a pre-stored background depth image. The topmost point of the human head region is extracted as the top of the head and the bottommost point of the human body region as the bottom of the foot. The depth value of the head top-point is corrected to a pixel value that has high similarity to a neighboring pixel. The position of the body bottom-point is corrected by calculating a depth gradient between vertically adjacent pixels. Two head-top and foot-bottom points are converted into 3D real-world coordinates using depth information. Two real-world coordinates estimate human height by measuring a Euclidean distance. Estimation errors for human height are corrected as the average of accumulated heights. In experiment results, we achieve that the estimated errors of human height with a standing state are 0.7% and 2.2% when the human body region is extracted by mask R-CNN and the background depth image, respectively.

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