A batch algorithm for estimating trajectories of point targets using expectation maximization

In this paper, we propose a strategy that is based on expectation maximization for tracking multiple point targets. The algorithm is similar to probabilistic multi-hypothesis tracking (PMHT) but does not relax the point target model assumptions. According to the point target models, a target can generate at most one measurement, and a measurement is generated by at most one target. With this model assumption, we show that the proposed algorithm can be implemented as iterations of Rauch-Tung-Striebel (RTS) smoothing for state estimation, and the loopy belief propagation method for marginal data association probabilities calculation. Using example illustrations with tracks, we compare the proposed algorithm with PMHT and joint probabilistic data association (JPDA) and show that PMHT and JPDA exhibit coalescence when there are closely moving targets whereas the proposed algorithm does not. Furthermore, extensive simulations c comparing the mean optimal subpattern assignment (MOSPA) performance of the algorithm for different scenarios averaged over several Monte Carlo iterations show that the proposed algorithm performs better than JPDA and PMHT. We also compare it to benchmarking algorithm: N-scan pruning based track-oriented multiple hypothesis tracking (TOMHT). The proposed algorithm shows a good tradeoff between computational complexity and the MOSPA performance

Data association algorithms and metric design for trajectory estimation

This thesis is concerned with trajectory estimation, which finds applications in various fields such as automotive safety and air traffic surveillance. More specifically, the thesis focuses on the data association part of the problem, for single and multiple targets, and on performance metrics. <br /><br />Data association for single-trajectory estimation is typically performed using Gaussian mixture smoothing. To limit complexity, pruning or merging approximations are used. In this thesis, we propose systematic ways to perform a combination of merging and pruning for two smoothing strategies: forward-backward smoothing (FBS) and two-filter smoothing (TFS). We present novel solutions to the backward smoothing step of FBS and a likelihood approximation, called smoothed posterior pruning, for the backward filtering in TFS. <br /><br />For data association in multi-trajectory estimation, we propose two iterative solutions based on expectation maximization (EM). The application of EM enables us to independently address the data association problems at different time instants, in each iteration. In the first solution, the best data association is estimated at each time instant using 2-D assignment, and given the best association, the states of the individual trajectories are immediately computed using Gaussian smoothing. In the second solution, we average the states of the individual trajectories over the data association distribution, which in turn is approximated using loopy belief propagation. Using simulations, we show that both solutions provide good trade-offs between accuracy and computation time compared to multiple hypothesis tracking.<br /><br />For evaluating the performance of trajectory estimation, we propose two metrics that behave in an intuitive manner, capturing the relevant features in target tracking. First, the generalized optimal sub-pattern assignment metric computes the distance between finite sets of states, and addresses properties such as localization errors and missed and false targets, which are all relevant to target estimation. The second metric computes the distance between sets of trajectories and considers the temporal dimension of trajectories. We refine the concepts of track switches, which allow a trajectory from one set to be paired with multiple trajectories in the other set across time, while penalizing it for these multiple assignments in an intuitive manner. We also present a lower bound for the metric that is remarkably accurate while being computable in polynomial time

Practical methods for Gaussian mixture filtering and smoothing

In many applications, there is an interest in systematically and sequentially estimating quantities of interest in a dynamical system, using indirect and inaccurate sensor observations. There are three important sub-problems of sequential estimation: prediction, filtering and smoothing. The objective in the prediction problem is to estimate the future states of the system, using the observations until the current point in time. In the filtering problem, we seek to estimate the current state of the system, using the same information and in the smoothing problem, the aim is to estimate a past state. The smoothing estimate has the advantage that it offers the best performance on average compared to filtering and prediction estimates. Often, the uncertainties regarding the system and the observations are modeled using Gaussian mixtures (GMs). The smoothing solutions to GMs are usually based on pruning approximations, which suffer from the degeneracy problem, resulting in inconsistent estimates. Solutions based on merging have not been explored well in the literature. We address the problem of GM smoothing using both pruning and merging approximations. We consider the two main smoothing strategies of forward-backward smoothing (FBS) and two-filter smoothing (TFS), and develop novel algorithms for GM smoothing which are specifically tailored for the two principles. The FBS strategy involves forward filtering followed by backward smoothing. The existing literature provides pruning-based solutions to the forward filtering and the backward smoothing steps involved. In this thesis, we present a novel solution to the backward smoothing step of FBS, when the forward filtering uses merging methods. The TFS method works by running two filtering steps: forward filtering and backward filtering. It is not possible to apply the pruning or merging strategies to the backward filtering, as it is not a density function. To the best of our knowledge, there does not exist practical approximation techniques to reduce the complexity of the backward filtering. Therefore, in this thesis we propose two novel techniques to approximate the output of the backward filtering, which we call intragroup approximation and smoothed posterior pruning. We also show that the smoothed posterior pruning technique is applicable to forward filtering as well. The FBS and TFS solutions based on the proposed ideas are implemented for a single target tracking scenario and are shown to have similar performance with respect to root mean squared error, normalized estimation error squared, computational complexity and track loss. Compared to the FBS based on N-scan pruning, both these algorithms provide estimates with high consistency and low complexity

Design and development of driving system for disabled driver

Having a specialized car for the disabled is a relatively new concept in Malaysia. Previously,disabled people in Malaysia had to import the system to be installed in their cars, where the cost of importing and installation is a big issue. Moreover, most control device used by physically handicapped drivers, especially people without lower limbs, are difficult to install and must be carefully adjusted to provide satisfactory performance. The main objective of the present study is to develop a new system in a way to modify the existing conventional car so that a disabled person without lower limbs can drive safely and properly

New challenges in studying nutrition-disease interactions in the developing world.

Latest estimates indicate that nutritional deficiencies account for 3 million child deaths each year in less-developed countries. Targeted nutritional interventions could therefore save millions of lives. However, such interventions require careful optimization to maximize benefit and avoid harm. Progress toward designing effective life-saving interventions is currently hampered by some serious gaps in our understanding of nutrient metabolism in humans. In this Personal Perspective, we highlight some of these gaps and make some proposals as to how improved research methods and technologies can be brought to bear on the problems of undernourished children in the developing world

The Impact of Visual Impairment on Functional Vision of Children in Rural South India: The Kariapatti Pediatric Eye Evaluation Project

PURPOSE. To determine the impact of visual impairment on functional vision of children in a rural population of south India. METHODS. A visual function questionnaire (LVP-VFQ) was administered to 1194 children aged 7 to 15 years identified through a systematic random sampling technique from 144 hamlets of Kariapatti in rural south India as part of a larger population-based project. Visual acuity estimations and clinical examinations for morbidity were performed in these 1194 children. A Rasch analysis was performed to validate the use of the instrument in this population. Bootstrap estimates (95% confidence intervals) of the regression coefficients were used to compare visual function scores between children with normal sight and children with uncorrected monocular and binocular visual impairment

The impact of different doses of vitamin A supplementation on male and female mortality. A randomised trial from Guinea-Bissau

<p>Abstract</p> <p>Background</p> <p>Vitamin A supplementation (VAS) given to children between 6 months and 5 years of age is known to reduce mortality in low-income countries. We have previously observed that girls benefit more from a lower dose of VAS than the one recommended by WHO, the effect being strongest if diphtheria-tetanus-pertussis vaccine (DTP) was the most recent vaccination. We aimed to test these observations.</p> <p>Methods</p> <p>During national immunisations days in Guinea-Bissau, West Africa, combining oral polio vaccination and VAS, we randomised 8626 children between 6 months and 5 years of age to receive the dose of VAS recommended by WHO or half this dose. Mortality rate ratios (MRRs) were assessed after 6 and 12 month.</p> <p>Results</p> <p>The overall mortality rate among participants was lower than expected. There was no significant difference in mortality at 6 months and 12 months of follow up between the low dose VAS group and the recommended dose VAS group. The MRRs were 1.23 (0.60-2.54) after 6 months and 1.17 (0.73-1.87) after 12 months. This tendency was similar in boys and girls. The low dose was not associated with lower mortality in girls if the most recent vaccine was DTP (MRR = 0.60 (0.14-2.50) after 6 months).</p> <p>Conclusion</p> <p>Our sample size does not permit firm conclusions since mortality was lower than expected. We could not confirm a beneficial effect of a lower dose of VAS on mortality in girls.</p> <p>Trial registration</p> <p>The study was registered under clinicaltrials.gov, number <a href="http://www.clinicaltrials.gov/ct2/show/NCT00168636">NCT00168636</a></p