IREE: Automated Vision Tracking of Project Related Entities

The purpose of this supplemental project was to collect invaluable data from the large-scale construction sites of Egnatia Odos motorway needed to validate a novel automated vision-tracking method created under the parent grant. For this purpose, one US graduate and three US undergraduate students traveled to Greece for 4 months and worked together with 2 Greek graduate students of the local faculty collaborator. This team of students monitored project activities and scheduled data collection trips on a daily basis, setup a mobile video data collection lab on the back of a truck, and drove to various sites every day to collect hundreds of hours of video from multiple cameras on a large variety of activities ranging from soil excavation to bridge construction. The US students were underrepresented students from minority groups who had never visited a foreign country. As a result, this trip was a major life experience to them. They learned how to live in a non-English speaking country, communicate with Greek students, workers and engineers. They lead a project in a very unfamiliar environment, troubleshoot myriad problems that hampered their progress daily and, above all, how to collaborate effectively and efficiently with other cultures. They returned to the US more mature, with improved leadership and problem-solving skills and a wider perspective of their profession.

Three-Dimensional Tracking of Construction Resources using an On-Site Camera System

Vision trackers have been proposed as a promising alternative for tracking at large-scale, congested construction sites. They provide the location of a large number of entities in a camera view across frames. However, vision trackers provide only two-dimensional (2D) pixel coordinates, which are not adequate for construction applications. This paper proposes and validates a method that overcomes this limitation by employing stereo cameras and converting 2D pixel coordinates to three-dimensional (3D) metric coordinates. The proposed method consists of four steps: camera calibration, camera pose estimation, 2D tracking, and triangulation. Given that the method employs fixed, calibrated stereo cameras with a long baseline, appropriate algorithms are selected for each step. Once the first two steps reveal camera system parameters, the third step determines 2D pixel coordinates of entities in subsequent frames. The 2D coordinates are triangulated on the basis of the camera system parameters to obtain 3D coordinates. The methodology presented in this paper has been implemented and tested with data collected from a construction site. The results demonstrate the suitability of this method for on-site tracking purposes.

The Gaussian mixture MCMC particle algorithm for dynamic cluster tracking

We present a novel filtering algorithm for tracking multiple clusters of coordinated objects. Based on a Markov chain Monte Carlo (MCMC) mechanism, the new algorithm propagates a discrete approximation of the underlying filtering density. A dynamic Gaussian mixture model is utilized for representing the time-varying clustering structure. This involves point process formulations of typical behavioral moves such as birth and death of clusters as well as merging and splitting. For handling complex, possibly large scale scenarios, the sampling efficiency of the basic MCMC scheme is enhanced via the use of a Metropolis within Gibbs particle refinement step. As the proposed methodology essentially involves random set representations, a new type of estimator, termed the probability hypothesis density surface (PHDS), is derived for computing point estimates. It is further proved that this estimator is optimal in the sense of the mean relative entropy. Finally, the algorithm's performance is assessed and demonstrated in both synthetic and realistic tracking scenarios. © 2012 Elsevier Ltd. All rights reserved.

Real-time block flow tracking of atrial septal defect motion in 4D cardiac ultrasound

Real-time cardiac ultrasound allows monitoring the heart motion during intracardiac beating heart procedures. Our application assists atrial septal defect (ASD) closure techniques using real-time 3D ultrasound guidance. One major image processing challenge is the processing of information at high frame rate. We present an optimized block flow technique, which combines the probability-based velocity computation for an entire block with template matching. We propose adapted similarity constraints both from frame to frame, to conserve energy, and globally, to minimize errors. We show tracking results on eight in-vivo 4D datasets acquired from porcine beating-heart procedures. Computing velocity at the block level with an optimized scheme, our technique tracks ASD motion at 41 frames/s. We analyze the errors of motion estimation and retrieve the cardiac cycle in ungated images. © 2007 IEEE.

Distributed maximum likelihood for simultaneous self-localization and tracking in sensor networks

We show that the sensor self-localization problem can be cast as a static parameter estimation problem for Hidden Markov Models and we implement fully decentralized versions of the Recursive Maximum Likelihood and on-line Expectation-Maximization algorithms to localize the sensor network simultaneously with target tracking. For linear Gaussian models, our algorithms can be implemented exactly using a distributed version of the Kalman filter and a novel message passing algorithm. The latter allows each node to compute the local derivatives of the likelihood or the sufficient statistics needed for Expectation-Maximization. In the non-linear case, a solution based on local linearization in the spirit of the Extended Kalman Filter is proposed. In numerical examples we demonstrate that the developed algorithms are able to learn the localization parameters. © 2012 IEEE.

Monte Carlo-Based Bayesian group object tracking and causal reasoning

We present algorithms for tracking and reasoning of local traits in the subsystem level based on the observed emergent behavior of multiple coordinated groups in potentially cluttered environments. Our proposed Bayesian inference schemes, which are primarily based on (Markov chain) Monte Carlo sequential methods, include: 1) an evolving network-based multiple object tracking algorithm that is capable of categorizing objects into groups, 2) a multiple cluster tracking algorithm for dealing with prohibitively large number of objects, and 3) a causality inference framework for identifying dominant agents based exclusively on their observed trajectories.We use these as building blocks for developing a unified tracking and behavioral reasoning paradigm. Both synthetic and realistic examples are provided for demonstrating the derived concepts. © 2013 Springer-Verlag Berlin Heidelberg.

A Monte Carlo expectation maximisation algorithm for multiple target tracking

In this paper, we present an expectation-maximisation (EM) algorithm for maximum likelihood estimation in multiple target models (MTT) with Gaussian linear state-space dynamics. We show that estimation of sufficient statistics for EM in a single Gaussian linear state-space model can be extended to the MTT case along with a Monte Carlo approximation for inference of unknown associations of targets. The stochastic approximation EM algorithm that we present here can be used along with any Monte Carlo method which has been developed for tracking in MTT models, such as Markov chain Monte Carlo and sequential Monte Carlo methods. We demonstrate the performance of the algorithm with a simulation. © 2012 ISIF (Intl Society of Information Fusi).

Oscillations in a decentralized economic network

The study of exchange markets dates back to LeonWalras's general equilibrium theory. Since then the economic market has been studied for its' equilibrium properties, fairness of allocations of private and public goods, and even the psychological incentives of participants. This paper studies the dynamics of an exchange economy built on a network of markets where consumers trade with suppliers to optimize utility. Viewing the market in as a decentralized network we study the system from the usual control theory point of view, evaluating the system's dynamic performance, stability and robustness. It is shown that certain consumer demand dynamics can lead to oscillations while others can converge to optimal allocations. © 2011 IFAC.

Single-sensor maximum power point tracking algorithms

Single-sensor maximum power point tracking algorithms for photovoltaic systems are presented. The algorithms have the features, characteristics and advantages of the widely used incremental conductance (INC) algorithm. However; unlike the INC algorithm which requires two sensors (the voltage sensor and the current sensor), the single-sensor algorithms are more desirable because they require only one sensor: the voltage sensor. The algorithms operate by maximising power at the DC-DC converter output, instead of the input. © 2013 The Institution of Engineering and Technology.