Estimating intrinsic camera parameters from the fundamental matrix using an evolutionary approach

Calibration is the process of computing the intrinsic (internal) camera parameters from a series of images. Normally calibration is done by placing predefined targets in the scene or by having special camera motions, such as rotations. If these two restrictions do not hold, then this calibration process is called autocalibration because it is done automatically, without user intervention. Using autocalibration, it is possible to create 3D reconstructions from a sequence of uncalibrated images without having to rely on a formal camera calibration process. The fundamental matrix describes the epipolar geometry between a pair of images, and it can be calculated directly from 2D image correspondences. We show that autocalibration from a set of fundamental matrices can simply be transformed into a global minimization problem utilizing a cost function. We use a stochastic optimization approach taken from the field of evolutionary computing to solve this problem. A number of experiments are performed on published and standardized data sets that show the effectiveness of the approach. The basic assumption of this method is that the internal (intrinsic) camera parameters remain constant throughout the image sequence, that is, the images are taken from the same camera without varying such quantities as the focal length. We show that for the autocalibration of the focal length and aspect ratio, the evolutionary method achieves results comparable to published methods but is simpler to implement and is efficient enough to handle larger image sequences

Estimation of projection matrices from a sparse set of feature points for 3D tree reconstruction from multiple images

3D reconstruction of trees is an important task for tree analysis but the most affordable approach to capture real objects is with a camera. Although, there already exist methods for 3D reconstruction of trees from multiple photographs, they mostly handle only self-standing trees captured at narrow angles. In fact, dense feature detection and matching is in most cases only the first step of the reconstruction and requires a large set of features and high similarity between individual pictures. However, capturing trees in the orchard is in most cases possible only at wider angles between the individual pictures and with overlapping branches from other trees, which prevents reliable feature matching. We introduce a new approach for estimating projection matrices to produce 3D point clouds of trees from multiple photographs. By manually relating a smaller number of points on images to reference objects, we substitute the missing dense set of features. We assign to each image a projection matrix and minimize the projection error between the images and reference objects using simulated annealing. Thereby, we produce correct projection matrices for further steps in 3D reconstruction. Our approach is tested on a simple application for 3D reconstruction of trees to produce a 3D point cloud. We analyze convergence rates of the optimization and show that the proposed approach can produce feasible projection matrices from a sufficiently large set of feature points. In the future, this approach will be a part of a complete system for tree reconstruction and analysis

Three dimensional information estimation and tracking for moving objects detection using two cameras framework

Calibration, matching and tracking are major concerns to obtain 3D information consisting of depth, direction and velocity. In finding depth, camera parameters and matched points are two necessary inputs. Depth, direction and matched points can be achieved accurately if cameras are well calibrated using manual traditional calibration. However, most of the manual traditional calibration methods are inconvenient to use because markers or real size of an object in the real world must be provided or known. Self-calibration can solve the traditional calibration limitation, but not on depth and matched points. Other approaches attempted to match corresponding object using 2D visual information without calibration, but they suffer low matching accuracy under huge perspective distortion. This research focuses on achieving 3D information using self-calibrated tracking system. In this system, matching and tracking are done under self-calibrated condition. There are three contributions introduced in this research to achieve the objectives. Firstly, orientation correction is introduced to obtain better relationship matrices for matching purpose during tracking. Secondly, after having relationship matrices another post-processing method, which is status based matching, is introduced for improving object matching result. This proposed matching algorithm is able to achieve almost 90% of matching rate. Depth is estimated after the status based matching. Thirdly, tracking is done based on x-y coordinates and the estimated depth under self-calibrated condition. Results show that the proposed self-calibrated tracking system successfully differentiates the location of objects even under occlusion in the field of view, and is able to determine the direction and the velocity of multiple moving objects

Object Detection and Tracking Using Uncalibrated Cameras

This thesis considers the problem of tracking an object in world coordinates using measurements obtained from multiple uncalibrated cameras. A general approach to track the location of a target involves different phases including calibrating the camera, detecting the object\u27s feature points over frames, tracking the object over frames and analyzing object\u27s motion and behavior. The approach contains two stages. First, the problem of camera calibration using a calibration object is studied. This approach retrieves the camera parameters from the known locations of ground data in 3D and their corresponding image coordinates. The next important part of this work is to develop an automated system to estimate the trajectory of the object in 3D from image sequences. This is achieved by combining, adapting and integrating several state-of-the-art algorithms. Synthetic data based on a nearly constant velocity object motion model is used to evaluate the performance of camera calibration and state estimation algorithms

Object Detection and Tracking Using Uncalibrated Cameras

This thesis considers the problem of tracking an object in world coordinates using measurements obtained from multiple uncalibrated cameras. A general approach to track the location of a target involves different phases including calibrating the camera, detecting the object\u27s feature points over frames, tracking the object over frames and analyzing object\u27s motion and behavior. The approach contains two stages. First, the problem of camera calibration using a calibration object is studied. This approach retrieves the camera parameters from the known locations of ground data in 3D and their corresponding image coordinates. The next important part of this work is to develop an automated system to estimate the trajectory of the object in 3D from image sequences. This is achieved by combining, adapting and integrating several state-of-the-art algorithms. Synthetic data based on a nearly constant velocity object motion model is used to evaluate the performance of camera calibration and state estimation algorithms

Bir krank biyel mekanizmasının ön kalibreli, 2 ticari kamera kullanılarak geliştirilen görüş sistemi vasıtasıyla kinematik analizi.

There are two main objectives of this study. The first objective is to develop a vision system consisting of 2 inexpensive commercial cameras. In general, by self – calibration methods reconstruction of a scene by using uncalibrated images is performed up to a scale only. However, in this thesis reconstruction of a scene is to be performed such that one obtains the actual values of the distances in the scene. For this purpose, it is assumed that the extrinsic parameters of the cameras are known. Therefore, one needs to determine the intrinsic parameters of the cameras only. In order to calculate the intrinsic parameters, two methods, that take advantage of the simplified Kruppa equations and the equal eigenvalue theorem, are used. The results obtained via the two methods are compared with the results obtained by using a calibration pattern. A triangulation process is then performed to calculate several known distances in the scene by using the method that gives better results for the intrinsic parameters. The actual and estimated distances obtained via the vision system are then presented and compared. The second objective of this study is to perform kinematic analysis of a slider crank mechanism by using the developed vision system. The position, velocity and acceleration analyses of the slider crank mechanism are realized by using several markers that are attached on the moving links of the mechanism. The positions of the markers are calculated by using the vision system. This data is then utilized to determine the joint variables, joint velocities and joint accelerations of the slider crank. The results thus obtained via an encoder attached to the input link of the mechanism are compared with the results obtained via the developed vision system. The effects of the locations of the markers and the effects of the number of markers used on the accuracy of the results are also investigated.M.S. - Master of Scienc

Analyse de séquences vidéo de surveillance basée sur la détection d'activités

Le présent mémoire porte sur des applications de vidéosurveillance fondées sur des techniques d'analyse d'images et de vidéos. Plus particulièrement, deux volets de la vidéosurveillance y sont abordés.Le premier volet porte sur la mise en correspondance d'objets vus par plusieurs caméras en même temps. Fonctionnant en réseau, ces caméras peuvent être fixes ou articulées, avoir différents paramètres internes (distance focale, résolution, etc.) et différentes positions et orientations. Ce type de réseau est qualifié d'hétérogène. À ce jour, très peu de solutions ont été proposées pour effectuer la mise en correspondance d'objets à travers un réseau hétérogène. L'originalité de notre méthode réside dans sa fonction de coût. Elle utilise la co-occurrence statistique d'événements binaires détectés par plusieurs caméras filmant un même endroit. L'utilisation de tels événements plutôt que des caractéristiques de couleur et de texture confère à notre méthode un avantage considérable. En effet, nous démontrons que la présence et l'absence d'activité sont des caractéristiques indépendantes de la position, de l'orientation ainsi que des paramètres internes des caméras. Autrement dit, un objet en mouvement vu par plusieurs caméras laissera une trace statistique identique dans chacune des caméras et ce, peu importe leur configuration. Notre méthode peut donc fonctionner sans étalonnage préalable du réseau, ce qui constitue un avantage indéniable. Nous démontrons également que les résultats obtenus par notre méthode peuvent être utilisés pour estimer des cartes d'occultation, les matrices d'homographie et fondamentale, ainsi que les matrices de projection des caméras.Le deuxième volet de ce mémoire porte sur la segmentation temporelle de longues séquences de vidéosurveillance. L'objectif ici est de segmenter une séquence vidéo longue de plusieurs heures en clips vidéo longs de quelques secondes. Ces clips sont étiquetés en fonction de la nature des événements qu'ils contiennent. Pour ce faire, nous utilisons à nouveau des événements binaires fondés sur la présence et l'absence d'activité. Ces événements nous permettent de quantifier non seulement la densité d'activité, mais également la taille des objets en mouvement, leur direction ainsi que leur vitesse. Dans ce mémoire, nous démontrons différentes façons d'extraire ces caractéristiques dites"événementielles". Nous comparons également différentes techniques de segmentation telles que la propagation d'affinité (l'affinity propagation), et la segmentation spectrale (spectral clustering) sur plusieurs vidéos de surveillance. Nous démontrons également que le positionnement multidimensionnel (multidimentional scaling) est un outil utile pour analyser le contenu sémantique de longues séquences vidéo