Towards automated visual surveillance using gait for identity recognition and tracking across multiple non-intersecting cameras

Despite the fact that personal privacy has become a major concern, surveillance technology is now becoming ubiquitous in modern society. This is mainly due to the increasing number of crimes as well as the essential necessity to provide secure and safer environment. Recent research studies have confirmed now the possibility of recognizing people by the way they walk i.e. gait. The aim of this research study is to investigate the use of gait for people detection as well as identification across different cameras. We present a new approach for people tracking and identification between different non-intersecting un-calibrated stationary cameras based on gait analysis. A vision-based markerless extraction method is being deployed for the derivation of gait kinematics as well as anthropometric measurements in order to produce a gait signature. The novelty of our approach is motivated by the recent research in biometrics and forensic analysis using gait. The experimental results affirmed the robustness of our approach to successfully detect walking people as well as its potency to extract gait features for different camera viewpoints achieving an identity recognition rate of 73.6 % processed for 2270 video sequences. Furthermore, experimental results confirmed the potential of the proposed method for identity tracking in real surveillance systems to recognize walking individuals across different views with an average recognition rate of 92.5 % for cross-camera matching for two different non-overlapping views.<br/

Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions

3D Tracking Using Multi-view Based Particle Filters

Visual surveillance and monitoring of indoor environments using multiple cameras has become a field of great activity in computer vision. Usual 3D tracking and positioning systems rely on several independent 2D tracking modules applied over individual camera streams, fused using geometrical relationships across cameras. As 2D tracking systems suffer inherent difficulties due to point of view limitations (perceptually similar foreground and background regions causing fragmentation of moving objects, occlusions), 3D tracking based on partially erroneous 2D tracks are likely to fail when handling multiple-people interaction. To overcome this problem, this paper proposes a Bayesian framework for combining 2D low-level cues from multiple cameras directly into the 3D world through 3D Particle Filters. This method allows to estimate the probability of a certain volume being occupied by a moving object, and thus to segment and track multiple people across the monitored area. The proposed method is developed on the basis of simple, binary 2D moving region segmentation on each camera, considered as different state observations. In addition, the method is proved well suited for integrating additional 2D low-level cues to increase system robustness to occlusions: in this line, a naïve color-based (HSI) appearance model has been integrated, resulting in clear performance improvements when dealing with complex scenarios

Active Estimation of Distance in a Robotic Vision System that Replicates Human Eye Movement

Many visual cues, both binocular and monocular, provide 3D information. When an agent moves with respect to a scene, an important cue is the different motion of objects located at various distances. While a motion parallax is evident for large translations of the agent, in most head/eye systems a small parallax occurs also during rotations of the cameras. A similar parallax is present also in the human eye. During a relocation of gaze, the shift in the retinal projection of an object depends not only on the amplitude of the movement, but also on the distance of the object with respect to the observer. This study proposes a method for estimating distance on the basis of the parallax that emerges from rotations of a camera. A pan/tilt system specifically designed to reproduce the oculomotor parallax present in the human eye was used to replicate the oculomotor strategy by which humans scan visual scenes. We show that the oculomotor parallax provides accurate estimation of distance during sequences of eye movements. In a system that actively scans a visual scene, challenging tasks such as image segmentation and figure/ground segregation greatly benefit from this cue.National Science Foundation (BIC-0432104, CCF-0130851

Modeling and applications of the focus cue in conventional digital cameras

El enfoque en cámaras digitales juega un papel fundamental tanto en la calidad de la imagen como en la percepción del entorno. Esta tesis estudia el enfoque en cámaras digitales convencionales, tales como cámaras de móviles, fotográficas, webcams y similares. Una revisión rigurosa de los conceptos teóricos detras del enfoque en cámaras convencionales muestra que, a pasar de su utilidad, el modelo clásico del thin lens presenta muchas limitaciones para aplicación en diferentes problemas relacionados con el foco. En esta tesis, el focus profile es propuesto como una alternativa a conceptos clásicos como la profundidad de campo. Los nuevos conceptos introducidos en esta tesis son aplicados a diferentes problemas relacionados con el foco, tales como la adquisición eficiente de imágenes, estimación de profundidad, integración de elementos perceptuales y fusión de imágenes. Los resultados experimentales muestran la aplicación exitosa de los modelos propuestos.The focus of digital cameras plays a fundamental role in both the quality of the acquired images and the perception of the imaged scene. This thesis studies the focus cue in conventional cameras with focus control, such as cellphone cameras, photography cameras, webcams and the like. A deep review of the theoretical concepts behind focus in conventional cameras reveals that, despite its usefulness, the widely known thin lens model has several limitations for solving different focus-related problems in computer vision. In order to overcome these limitations, the focus profile model is introduced as an alternative to classic concepts, such as the near and far limits of the depth-of-field. The new concepts introduced in this dissertation are exploited for solving diverse focus-related problems, such as efficient image capture, depth estimation, visual cue integration and image fusion. The results obtained through an exhaustive experimental validation demonstrate the applicability of the proposed models

Homography-based ground plane detection using a single on-board camera

This study presents a robust method for ground plane detection in vision-based systems with a non-stationary camera. The proposed method is based on the reliable estimation of the homography between ground planes in successive images. This homography is computed using a feature matching approach, which in contrast to classical approaches to on-board motion estimation does not require explicit ego-motion calculation. As opposed to it, a novel homography calculation method based on a linear estimation framework is presented. This framework provides predictions of the ground plane transformation matrix that are dynamically updated with new measurements. The method is specially suited for challenging environments, in particular traffic scenarios, in which the information is scarce and the homography computed from the images is usually inaccurate or erroneous. The proposed estimation framework is able to remove erroneous measurements and to correct those that are inaccurate, hence producing a reliable homography estimate at each instant. It is based on the evaluation of the difference between the predicted and the observed transformations, measured according to the spectral norm of the associated matrix of differences. Moreover, an example is provided on how to use the information extracted from ground plane estimation to achieve object detection and tracking. The method has been successfully demonstrated for the detection of moving vehicles in traffic environments

Object Tracking and Mensuration in Surveillance Videos

This thesis focuses on tracking and mensuration in surveillance videos. The first part of the thesis discusses several object tracking approaches based on the different properties of tracking targets. For airborne videos, where the targets are usually small and with low resolutions, an approach of building motion models for foreground/background proposed in which the foreground target is simplified as a rigid object. For relatively high resolution targets, the non-rigid models are applied. An active contour-based algorithm has been introduced. The algorithm is based on decomposing the tracking into three parts: estimate the affine transform parameters between successive frames using particle filters; detect the contour deformation using a probabilistic deformation map, and regulate the deformation by projecting the updated model onto a trained shape subspace. The active appearance Markov chain (AAMC). It integrates a statistical model of shape, appearance and motion. In the AAMC model, a Markov chain represents the switching of motion phases (poses), and several pairwise active appearance model (P-AAM) components characterize the shape, appearance and motion information for different motion phases. The second part of the thesis covers video mensuration, in which we have proposed a heightmeasuring algorithm with less human supervision, more flexibility and improved robustness. From videos acquired by an uncalibrated stationary camera, we first recover the vanishing line and the vertical point of the scene. We then apply a single view mensuration algorithm to each of the frames to obtain height measurements. Finally, using the LMedS as the cost function and the Robbins-Monro stochastic approximation (RMSA) technique to obtain the optimal estimate