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.
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 @cite_12 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.
@cite_12
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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. This paper presents a novel technique for the estimation of the height of an object using a single camera view. In the proposed method, the only information required is the knowledge about the pose of the camera with respect to the world (i.e., height and pitch angle of the camera with respect to the ground) and a vanishing point. In the developed theory, the focal length may also be known, but in the proposed experiments it has not been employed: an approximation for small pitch angles has been taken into account and the consequent committed error has been then analysed. The presented method gives accurate results for any object placed in unstructured environments, regardless of the relative distance from the camera. The method has been tested in a series of outdoor and indoor environments, and the experimental results are presented in this paper. In recent years, the task of estimating the 6D pose of object instances and complete scenes, i.e. camera localization, from a single input image has received considerable attention. Consumer RGB-D cameras have made this feasible, even for difficult, texture-less objects and scenes. In this work, we show that a single RGB image is sufficient to achieve visually convincing results. Our key concept is to model and exploit the uncertainty of the system at all stages of the processing pipeline. The uncertainty comes in the form of continuous distributions over 3D object coordinates and discrete distributions over object labels. We give three technical contributions. Firstly, we develop a regularized, auto-context regression framework which iteratively reduces uncertainty in object coordinate and object label predictions. Secondly, we introduce an efficient way to marginalize object coordinate distributions over depth. This is necessary to deal with missing depth information. Thirdly, we utilize the distributions over object labels to detect multiple objects simultaneously with a fixed budget of RANSAC hypotheses. We tested our system for object pose estimation and camera localization on commonly used data sets. We see a major improvement over competing systems. In this paper we introduce a probabilistic approach on multiple person localization using multiple calibrated camera views. People present in the scene are approximated by a population of cylinder objects in the 3-D world coordinate system, which is a realization of a Marked Point Process. The observation model is based on the projection of the pixels of the obtained motion masks in the different camera images to the ground plane and to other parallel planes with different height. The proposed pixel-level feature is based on physical properties of the 2-D image formation process and can accurately localize the leg position on the ground plane and estimate the height of the people, even if the area of interest is only a part of the scene, meanwhile silhouettes from irrelevant outside motions may significantly overlap with the monitored region in some of the camera views. We introduce an energy function, which contains a data term calculated from the extracted features and a geometrical constraint term modeling the distance between two people. The final configuration results (location and height) are obtained by an iterative stochastic energy optimization process, called the Multiple Birth and Death dynamics. The proposed approached is compared to a recent state-of-the-art technique in a publicly available dataset and its advantages are quantitatively demonstrated.