A Multiobjective Approach to Homography Estimation

In several machine vision problems, a relevant issue is the estimation of homographies between two different perspectives that hold an extensive set of abnormal data. A method to find such estimation is the random sampling consensus (RANSAC); in this, the goal is to maximize the number of matching points given a permissible error (Pe), according to a candidate model. However, those objectives are in conflict: a low Pe value increases the accuracy of the model but degrades its generalization ability that refers to the number of matching points that tolerate noisy data, whereas a high Pe value improves the noise tolerance of the model but adversely drives the process to false detections. This work considers the estimation process as a multiobjective optimization problem that seeks to maximize the number of matching points whereas Pe is simultaneously minimized. In order to solve the multiobjective formulation, two different evolutionary algorithms have been explored: the Nondominated Sorting Genetic Algorithm II (NSGA-II) and the Nondominated Sorting Differential Evolution (NSDE). Results considering acknowledged quality measures among original and transformed images over a well-known image benchmark show superior performance of the proposal than Random Sample Consensus algorithm

Detección de vehículos en entornos multi-cámara utilizando información contextual

Dada la amplia demanda que actualmente existe en el área de la video-seguridad, se ha producido un aumento en el número de investigaciones que se derivan de este campo. En particular, con el fin de facilitar el control de los parkings, se presenta un sistema multicámara para la detección de vehículos y su correspondiente asignación a las plazas ocupadas por los mismos dentro del aparcamiento, obteniendo una visión de aquellas plazas que están disponibles para ser ocupadas. Gracias a este sistema, se puede sustituir con el uso de la visión artificial el método habitual de instalación de sensores de inducción o de peso y movimiento, los cuales encarecen considerablemente el despliegue y gestión de estos aparcamientos. De cara a proporcionar sistemas cada vez más eficientes, son muchos los algoritmos que han surgido para la detección de objetos y sus características. En concreto en este trabajo se hace uso de dos algoritmos de detección de objetos, Deformable Parts Model (DPM) y un detector de regiones mediante redes neuronales convolucionales, Faster Regions with Convolutional Neural Network (Faster R-CNN). Ambos han sido probados en trabajos previos. Con el fin de mejorar estos sistemas de detección de vehículos, se propone la integración de la información del entorno captada por un conjunto de cámaras, de manera que la fusión de esta información proporciona un mayor rendimiento a la hora de realizar detecciones dentro del aparcamiento. Gracias a ello, detecciones que no se pueden llevar a cabo desde un punto de vista por posibles oclusiones o grandes distancias son ahora posibles puesto que se completa esta información con la que otro punto de vista (u otros) proporciona

3D Pedestrian Tracking and Virtual Reconstruction of Ceramic Vessels Using Geometric and Color Cues

Object tracking using cameras has many applications ranging from monitoring children and the elderly, to behavior analysis, entertainment, and homeland security. This thesis concentrates on the problem of tracking person(s) of interest in crowded scenes (e.g., airports, train stations, malls, etc.), rendering their locations in time and space along with high quality close-up images of the person for recognition. The tracking is achieved using a combination of overhead cameras for 3D tracking and a network of pan-tilt-zoom (PTZ) cameras to obtain close-up frontal face images. Based on projective geometry, the overhead cameras track people using salient and easily computable feature points such as head points. When the obtained head point is not accurate enough, the color information of the head tops across subsequent frames is integrated to detect and track people. To capture the best frontal face images of a target across time, a PTZ camera scheduling is proposed, where the 'best' PTZ camera is selected based on the capture quality (as close as possible to frontal view) and handoff success (response time needed by the newly selected camera to move from current to desired state) probabilities. The experiments show the 3D tracking errors are very small (less than 5 cm with 14 people crowding an area of around 4 m2) and the frontal face images are captured effectively with most of them centering in the frames. Computational archaeology is becoming a success story of applying computational tools in the reconstruction of vessels obtained from digs, freeing the expert from hours of intensive labor in manually stitching shards into meaningful vessels. In this thesis, we concentrate on the use of geometric and color information of the fragments for 3D virtual reconstruction of broken ceramic vessels. Generic models generated by the experts as a rendition of what the original vessel may have looked like are also utilized. The generic models need not to be identical to the original vessel, but are within a geometric transformation of it in most of its parts. The markings on the 3D surfaces of fragments and generic models are extracted based on their color cues. Ceramic fragments are then aligned against the corresponding generic models based on the geometric relation between the extracted markings. The alignments yield sub-scanner resolution fitting errors.Ph.D., Electrical Engineering -- Drexel University, 201

The utility of gait as a biological characteristic in forensic investigations – An empirical examination of movement pattern variation using biomechanical and anthropological principles

Forensic gait analysis is generally defined as the analysis of gait features from video footage to assist in criminal investigations. Although an attractive means to detect suspects since data can be collected from a distance without their knowledge, forensic gait analysis presently lacks method validation and quality standards, not only due to insufficient research, but also because certain scientific foundations, such as the assumption of gait uniqueness, have not been adequately addressed. To test the scientific basis of this premise, a suitable dataset replicating an ideal forensic gait analysis scenario was compiled from the Karlsruhe Institute of Technology (Germany) database. Biomechanical analysis of sagittal plane human motion in the bilateral shoulder, elbow, hip, knee, and ankle joints was conducted across complete gait cycles of twenty participants, to investigate the degree to which intraindividual variation impacts interindividual variation, according to the following aims: (1) to better understand the relationship between form (anatomy) and function (physiology) of human gait, (2) to investigate the basis of gait uniqueness by examining similarities and differences in joint angles, and (3) to build upon current theoretical foundations of gait-based human identification. The findings indicate different degrees of movement asymmetry given body region and gait sub-phase, thereby challenging previous methods employing interchangeable use of bilateral motion data, and the use of ‘average’ gait cycles to represent the gait of an individual irrespective of body side. Furthermore, interindividual variability in all five joints is influenced by body side to different extents depending on gait sub-phase and body region, thereby challenging the claim of holistic uniqueness of gait features across all body regions and gait events. Given the findings of this thesis and paucity regarding empirical basis to support expertise, exerting caution when evaluating gait-based evidence admissibility is highly recommended, since the utility of gait in identification is currently limited

Robust moving object detection by information fusion from multiple cameras

Moving object detection is an essential process before tracking and event recognition in video surveillance can take place. To monitor a wider field of view and avoid occlusions in pedestrian tracking, multiple cameras are usually used and homography can be employed to associate multiple camera views. Foreground regions detected from each of the multiple camera views are projected into a virtual top view according to the homography for a plane. The intersection regions of the foreground projections indicate the locations of moving objects on that plane. The homography mapping for a set of parallel planes at different heights can increase the robustness of the detection. However, homography mapping is very time consuming and the intersections of non-corresponding foreground regions can cause false-positive detections. In this thesis, a real-time moving object detection algorithm using multiple cameras is proposed. Unlike the pixelwise homography mapping which projects binary foreground images, the approach used in the research described in this thesis was to approximate the contour of each foreground region with a polygon and only transmit and project the polygon vertices. The foreground projections are rebuilt from the projected polygons in the reference view. The experimental results have shown that this method can be run in real time and generate results similar to those using foreground images. To identify the false-positive detections, both geometrical information and colour cues are utilized. The former is a height matching algorithm based on the geometry between the camera views. The latter is a colour matching algorithm based on the Mahalanobis distance of the colour distributions of two foreground regions. Since the height matching is uncertain in the scenarios with the adjacent pedestrian and colour matching cannot handle occluded pedestrians, the two algorithms are combined to improve the robustness of the foreground intersection classification. The robustness of the proposed algorithm is demonstrated in real-world image sequences