You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Designing robust activity detectors for fixed camera surveillance video requires knowledge of the 3-D scene. This paper presents an automatic camera calibration process that provides a mechanism to reason about the spatial proximity between objects at different times. It combines a CNN-based camera pose estimator with a vertical scale provided by pedestrian observations to establish the 4-D scene geometry. Unlike some previous methods, the people do not need to be tracked nor do the head and feet need to be explicitly detected. It is robust to individual height variations and camera parameter estimation errors.

Cited Abstract(s):
[1]: In this thesis three different algorithms to estimate the height of an object are investigated. The investigation mainly focuses on how accurate the height estimation is and how user friendly it is to calibrate. In order to determine that a survey was conducted in which individuals had to calibrate each algorithm and then answer to some questions about how they experienced the process.The first algorithm was a re-implementation of a method available in literature, which measures distance between two parallel planes by finding the ground plane's vanishing line and the vanishing point in the reference direction. The second algorithm estimates the object's world scene's 3D pose by reversing the projection made by the camera when the image was captured. The third and last algorithm investigated estimates the height of the object by using information about how many pixels a reference object, of know height, occupies when it appears at different locations in the image.The company, on behalf which this thesis was written, wants to use the information about an object's height in order to build a classifier capable of distinguishing pets from burglars. The algorithm most suitable for that task was simulated to test how sensitive it is to noisy input. Finally that algorithm was recommended to the company.
Estimating the height of objects in images is closely related to 3-D location estimation. As is described in Section , height estimations can be combined with camera parameters to establish a projection matrix from 3-D world coordinates to the 2-D image plane. Vester [1] provides an overview and analysis of common techniques for height estimation. The first algorithm presented utilizes two user-defined parallel planes to determine an object's height. The second algorithm presented utilizes knowledge of the camera's position and orientation relative to the ground plane as well as a known object height to perform a back projection to 3-D coordinates. Although the formulation slightly differs, this process is most similar to the process used in this paper, with the caveat that this paper estimates the camera's position and orientation and object heights. The final algorithm presented involves setting up 5 reference objects with known heights, one at each corner of the picture and one in the center of the picture. These heights are then used to calculate the height of an unknown object.