# Human Height Estimation by Color Deep Learning and Depth 3D Conversion
## 2 Related Works
### Object Length Measurement from Color or Depth Information  
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. The object length is estimated by converting the 2D coordinates in the pixels of the image into 3D coordinates through the projection model. The length estimation methods by the camera parameters have a disadvantage that the accurate camera parameters should be provided in advance. In order to overcome this disadvantage, [PERSON] [2] introduces an estimation method for the camera parameters using prior knowledge about the distribution of relative human heights. [PERSON] [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. The vanishing point is found by detecting the straight lines in the image. The length ratio between two objects can be calculated using the vanishing points. If the length of one object is known in advance, then the length of another object can be calculated by the length ratio. [PERSON] [7] introduces a length estimation method by the given vanishing points of the ground. [PERSON] [8] proposes the detection method of the vanishing points by clustering the straight lines iteratively without camera calibration. [PERSON] [9] proposes the method of detecting the vanishing points for color videos captured by multiple cameras. [PERSON] [10] proposes an error model for the human-height estimation by the vanishing points. The error of the human-height estimation is corrected by the error model. [PERSON] [11] detects the vanishing points by tracking specific object such as traffic signs from a sequence of frames. [PERSON] [12] estimates the object length by detecting the specific objects. Human height can be also estimated by the prior statistical knowledge of human anthropometry [28; 29] or by the gaits [30; 31]. In recent years, estimation studies in various fields achieve great success by applying neural networks. The neural networks are also applied to the human-height estimation. [PERSON] [32] proposes a neural network for predicting a relationship between each proportion of human joints and human height. [PERSON] [33] estimates human height by CNN using a length ratio between a human body width and a human head size.  
Since depth video has distance information from the depth camera, the distance between two points in a depth image can be measured without the camera parameters or the vanishing points. Many studies [34; 35; 36] extract a skeleton, which is the connection structure of human body parts, from the depth image for the human-height estimation. However, the human body region extraction is some inaccurate due to noises in depth video.