Efficient height measurements in single images based on the detection of vanishing points

Andaló, Fernanda A.; Taubin, Gabriel; Goldenstein, Siome

doi:10.1016/j.cviu.2015.03.017

Cited by 19 publications

(13 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Vanishing point detection has long been an active research topic in computer vision with many real-world applications such as camera calibration [16], [17], pose estimation [18], [19], [20], height measurement [21], object detection [22], and 3D reconstruction [23], [24]. Since the VPs can be represented by normalized 2D homogeneous coordinates on a Gaussian sphere, early works on VP detection use a Hough transform of the line segments on the Gaussian sphere [25], [26], [27], [28] or a cube map [29].…”

Section: A Vanishing Point Detectionmentioning

confidence: 99%

Detecting Dominant Vanishing Points in Natural Scenes with Application to Composition-Sensitive Image Retrieval

Zhou

Farhat

Wang

2017

IEEE Trans. Multimedia

View full text Add to dashboard Cite

Abstract-Linear perspective is widely used in landscape photography to create the impression of depth on a 2D photo. Automated understanding of linear perspective in landscape photography has several real-world applications, including aesthetics assessment, image retrieval, and on-site feedback for photo composition, yet adequate automated understanding has been elusive. We address this problem by detecting the dominant vanishing point and the associated line structures in a photo. However, natural landscape scenes pose great technical challenges because often the inadequate number of strong edges converging to the dominant vanishing point is inadequate. To overcome this difficulty, we propose a novel vanishing point detection method that exploits global structures in the scene via contour detection. We show that our method significantly outperforms state-of-the-art methods on a public ground truth landscape image dataset that we have created. Based on the detection results, we further demonstrate how our approach to linear perspective understanding provides on-site guidance to amateur photographers on their work through a novel viewpoint-specific image retrieval system.

show abstract

Section: A Vanishing Point Detectionmentioning

confidence: 99%

Detecting Dominant Vanishing Points in Natural Scenes with Application to Composition-Sensitive Image Retrieval

Zhou

Farhat

Wang

2017

IEEE Trans. Multimedia

View full text Add to dashboard Cite

show abstract

“…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%

“…Both the position and the pose of the camera are required in order to obtain 3D information of human body. Human height can also be estimated by calculating the ratio of the length between human body and a reference object whose length is already known [7][8][9][10][11][12]. The estimation methods of human height based on color video have a disadvantage in that the camera parameters or information about a reference object are required.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

“…Zhao and Hu used a pure translation to calibrate a camera [19], and Li et al reduced control points given the intrinsic camera parameters to calibrate a pantilt camera [20]. Andaló et al estimated vanishing points by clustering lines in an image and then calculated the object height [21]. However, this method cannot accurately estimate vanishing points when the background does not include sufficient pairs of parallel lines.…”

Section: Introductionmentioning

confidence: 99%

Object Detection and Tracking-Based Camera Calibration for Normalized Human Height Estimation

et al. 2016

View full text Add to dashboard Cite

This paper presents a normalized human height estimation algorithm using an uncalibrated camera. To estimate the normalized human height, the proposed algorithm detects a moving object and performs tracking-based automatic camera calibration. The proposed method consists of three steps: (i) moving human detection and tracking, (ii) automatic camera calibration, and (iii) human height estimation and error correction. The proposed method automatically calibrates camera by detecting moving humans and estimates the human height using error correction. The proposed method can be applied to object-based video surveillance systems and digital forensic.

show abstract

Efficient height measurements in single images based on the detection of vanishing points

Cited by 19 publications

References 27 publications

Detecting Dominant Vanishing Points in Natural Scenes with Application to Composition-Sensitive Image Retrieval

Detecting Dominant Vanishing Points in Natural Scenes with Application to Composition-Sensitive Image Retrieval

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

Object Detection and Tracking-Based Camera Calibration for Normalized Human Height Estimation

Contact Info

Product

Resources

About