Automated Markerless Extraction of Walking People Using Deformable Contour Models

We develop a new automated markerless motion capture system for the analysis of walking people. We employ global evidence gathering techniques guided by biomechanical analysis to robustly extract articulated motion. This forms a basis for new deformable contour models, using local image cues to capture shape and motion at a more detailed level. We extend the greedy snake formulation to include temporal constraints and occlusion modelling, increasing the capability of this technique when dealing with cluttered and self-occluding extraction targets. This approach is evaluated on a large database of indoor and outdoor video data, demonstrating fast and autonomous motion capture for walking people

A smart environment for biometric capture

The development of large scale biometric systems require experiments to be performed on large amounts of data. Existing capture systems are designed for fixed experiments and are not easily scalable. In this scenario even the addition of extra data is difficult. We developed a prototype biometric tunnel for the capture of non-contact biometrics. It is self contained and autonomous. Such a configuration is ideal for building access or deployment in secure environments. The tunnel captures cropped images of the subject's face and performs a 3D reconstruction of the person's motion which is used to extract gait information. Interaction between the various parts of the system is performed via the use of an agent framework. The design of this system is a trade-off between parallel and serial processing due to various hardware bottlenecks. When tested on a small population the extracted features have been shown to be potent for recognition. We currently achieve a moderate throughput of approximate 15 subjects an hour and hope to improve this in the future as the prototype becomes more complete

A new twist to an old story: HE 0450-2958, and the ULIRG→\to (optically bright QSO) transition hypothesis

We report on interferometric imaging of the CO J=1--0 and J=3--2 line emission from the controversial QSO/galaxy pair HE 0450--2958. {\it The detected CO J=1--0 line emission is found associated with the disturbed companion galaxy not the luminous QSO,} and implies $\rm M_{gal}(H_2)\sim (1-2)\times 10^{10} M_{\odot}$, which is \ga 30% of the dynamical mass in its CO-luminous region. Fueled by this large gas reservoir this galaxy is the site of an intense starburst with $\rm SFR\sim 370 M_{\odot} yr^{-1}$, placing it firmly on the upper gas-rich/star-forming end of Ultra Luminous Infrared Galaxies (ULIRGs, $\rm L_{IR}>10^{12} L_{\odot}$). This makes HE 0450--2958 the first case of extreme starburst and powerful QSO activity, intimately linked (triggered by a strong interaction) but not coincident. The lack of CO emission towards the QSO itself renews the controversy regarding its host galaxy by making a gas-rich spiral (the typical host of Narrow Line Seyfert~1 AGNs) less likely. Finally, given that HE 0450--2958 and similar IR-warm QSOs are considered typical ULIRG$\to$(optically bright QSO) transition candidates, our results raise the possibility that some may simply be {\it gas-rich/gas-poor (e.g. spiral/elliptical) galaxy interactions} which ``activate'' an optically bright unobscured QSO in the gas-poor galaxy, and a starburst in the gas-rich one. We argue that such interactions may have gone largely unnoticed even in the local Universe because the combination of tools necessary to disentagle the progenitors (high resolution and S/N optical {\it and} CO imaging) became available only recently.Comment: 25 pages, 5 figures, accepted for publication by The Astrophysical Journa

Investigation of the inerter-based dynamic vibration absorber for machining chatter suppression

This study proposes a novel approach to increase the chatter stability in machining operations. It shows the potential performance improvement when an inerter-based dynamic vibration absorber is employed in a machining operation. Tuned inerter based devices have been employed to decrease the magnitude of the vibrations in applications such as civil engineering structures and vehicle suspension systems but the nature of chatter in machining is different from these applications. Therefore, it requires a different tuning methodology to obtain the optimal design parameters. In this study, the machining operation is modelled as an undamped single degree of freedom system and different configurations of an inerter, a damper and two springs are used to ensure a stable region of operation. Strategies for the tuning parameters are developed both analytically and numerically. Using these techniques the performance improvement in the chatter stability provided by using inerter based devices instead of a traditional dynamic vibration absorber is demonstrated

Improving the vibration suppression capabilities of a magneto-rheological damper using hybrid active and semi-active control

This paper presents a new hybrid active & semi-active control method for vibration suppression in flexible structures. The method uses a combination of a semi-active device and an active control actuator situated elsewhere in the structure to suppress vibrations. The key novelty is to use the hybrid controller to enable the magneto-rheological damper to achieve a performance as close to a fully active device as possible. This is achieved by ensuring that the active actuator can assist the magneto-rheological damper in the regions where energy is required. In addition, the hybrid active & semi-active controller is designed to minimize the switching of the semi-active controller. The control framework used is the immersion and invariance control technique in combination with sliding mode control. A two degree-of-freedom system with lightly damped resonances is used as an example system. Both numerical and experimental results are generated for this system, and then compared as part of a validation study. The experimental system uses hardware-in-the-loop to simulate the effect of both the degrees-of-freedom. The results show that the concept is viable both numerically and experimentally, and improved vibration suppression results can be obtained for the magneto-rheological damper that approach the performance of an active device

On Automated Model-Based Extraction and Analysis of Gait

We develop a new model-based extraction process guided by biomechanical analysis for walking people, and analyse its data for recognition capability. Hierarchies of shape and motion yield relatively modest computational demands, while anatomical data is used to generate shape models consistent with normal human body proportions. Mean gait data is used to create prototype gait motion models, which are adapted to fit individual subjects. Our approach is evaluated on a large gait database, comprising 4824 sequences from 115 subjects, demonstrating gait extraction and description capability in laboratory and real-world capture conditions. Recognition capability is illustrated by an 84% CCR in laboratory conditions, which is reduced for real-world (outdoor) data. Preliminary results from a statistical analysis of the extracted gait parameters, suggest that recognition capability is primarily gained from cadence and from static shape parameters, although gait is the cue by which these are derived

Model-Based Gait Enrolment in Real-World Imagery

We present a model-based approach to gait extraction that is capable of reliable operation on real-world imagery. Hierarchies of shape and motion are employed to yield relatively modest computational demands, avoiding the high-dimensional search spaces associated with complex models. Anatomical data is used to generate shape models consistent with normal human body proportions. Mean gait data is used to create prototype gait motion models, which are adapted to fit individual subjects. Accuracy is evaluated on subjects filmed from a fronto-parallel view in controlled laboratory conditions, for which some gait parameters are known. We further show that comparable performance is attained in outdoor conditions. As such, we describe a new approach to enrolment for gait recognition technologies, allowing reliable subject gait extraction in real-world imagery

Local and Global Models for Articulated Motion Analysis

Vision is likely the most important of the senses employed by humans in understanding their environment, but computer systems are still sorely lacking in this respect. The number of potential applications for visually capable computer systems is huge; this thesis focuses on the field of motion capture, in particular dealing with the problems encountered when analysing the motion of articulated or jointed targets, such as people. Joint articulation greatly increases the complexity of a target object, and increases the incidence of self-occlusion (one body part obscuring another). These problems are compounded in typical outdoor scenes by the clutter and noise generated by other objects. This thesis presents a model-based approach to automated extraction of walking people from video data, under indoor and outdoor capture conditions. Local and global modelling strategies are employed in an iterative process, similar to the Generalised Expectation-Maximisation algorithm. Prior knowledge of human shape, gait motion and self-occlusion is used to guide this extraction process. The extracted shape and motion information is applied to construct a gait signature, sufficient for recognition purposes. Results are presented demonstrating the success of this approach on the Southampton Gait Database, comprising 4820 sequences from 115 subjects. A recognition rate of 98.6% is achieved on clean indoor data, comparing favourably with other published approaches. This recognition rate is reduced to 87.1% under the more difficult outdoor capture conditions. Additional analyses are presented examining the discriminative potential of model features. It is shown that the majority of discriminative potential is contained within body shape features and gait frequency, although motion dynamics also make a significant contribution

Alâkatu'l-Kurân bi'ş-Şi'r

The development of large scale biometric systems requires experiments to be performed on large amounts of data. Existing capture systems are designed for fixed experiments and are not easily scalable. In this scenario even the addition of extra data is difficult. We developed a prototype \emph{biometric tunnel} for the capture of non-contact biometrics. It is self contained and autonomous. Such a configuration is ideal for building access or deployment in secure environments. The tunnel captures cropped images of the subject's face and performs a 3D reconstruction of the person's motion which is used to extract gait information. Interaction between the various parts of the system is performed via the use of an agent framework. The design of this system is a trade-off between parallel and serial processing due to various hardware bottlenecks. When tested on a small population the extracted features have been shown to be potent for recognition. We currently achieve a moderate throughput of approximate 15 subjects an hour and hope to improve this in the future as the prototype becomes more complete