Bayesian-based techniques for tracking multiple humans in an enclosed environment

This thesis deals with the problem of online visual tracking of multiple humans in an enclosed environment. The focus is to develop techniques to deal with the challenges of varying number of targets, inter-target occlusions and interactions when every target gives rise to multiple measurements (pixels) in every video frame. This thesis contains three different contributions to the research in multi-target tracking. Firstly, a multiple target tracking algorithm is proposed which focuses on mitigating the inter-target occlusion problem during complex interactions. This is achieved with the help of a particle filter, multiple video cues and a new interaction model. A Markov chain Monte Carlo particle filter (MCMC-PF) is used along with a new interaction model which helps in modeling interactions of multiple targets. This helps to overcome tracking failures due to occlusions. A new weighted Markov chain Monte Carlo (WMCMC) sampling technique is also proposed which assists in achieving a reduced tracking error. Although effective, to accommodate multiple measurements (pixels) produced by every target, this technique aggregates measurements into features which results in information loss. In the second contribution, a novel variational Bayesian clustering-based multi-target tracking framework is proposed which can associate multiple measurements to every target without aggregating them into features. It copes with complex inter-target occlusions by maintaining the identity of targets during their close physical interactions and handles efficiently a time-varying number of targets. The proposed multi-target tracking framework consists of background subtraction, clustering, data association and particle filtering. A variational Bayesian clustering technique groups the extracted foreground measurements while an improved feature based joint probabilistic data association filter (JPDAF) is developed to associate clusters of measurements to every target. The data association information is used within the particle filter to track multiple targets. The clustering results are further utilised to estimate the number of targets. The proposed technique improves the tracking accuracy. However, the proposed features based JPDAF technique results in an exponential growth of computational complexity of the overall framework with increase in number of targets. In the final work, a novel data association technique for multi-target tracking is proposed which more efficiently assigns multiple measurements to every target, with a reduced computational complexity. A belief propagation (BP) based cluster to target association method is proposed which exploits the inter-cluster dependency information. Both location and features of clusters are used to re-identify the targets when they emerge from occlusions. The proposed techniques are evaluated on benchmark data sets and their performance is compared with state-of-the-art techniques by using, quantitative and global performance measures

Application of improved particle filter in multiple maneuvering target tracking system

Ph.DDOCTOR OF PHILOSOPH

Robust Multi-target Tracking with Bootstrapped-GLMB Filter

This dissertation presents novel multi-target tracking algorithms that obviate the need for prior knowledge of system parameters such as clutter rate, detection probabilities, and birth models. Information on these parameters is unknown but important to tracking performance. The proposed algorithms exploit the advantages of existing RFS trackers and filters by bootstrapping them. This configuration inherits the efficiency of tracking target trajectories from the RFS trackers and low complexity in parameter estimation from the RFS filters

Data Association in a World Model for Autonomous Systems

This contribution introduces a three pillar information storage and management system for modeling the environment of autonomous systems. The main characteristics is the separation of prior knowledge, environment model and sensor information. In the center of the system is the environment model, which provides the autonomous system with information about the current state of the environment. It consists of instances with attributes and relations as virtual substitutes of entities (persons and objects) of the real world. Important features are the representation of uncertain information by means of Degree-of-Belief (DoB) distributions, the information exchange between the three pillars as well as creation, deletion and update of instances, attributes and relations in the environment model. In this work, a Bayesian method for fusing new observations to the environment model is introduced. For this purpose, a Bayesian data association method is derived. The main question answered here is the observation-to-instance mapping and the decision mechanisms for creating a new instance or updating already existing instances in the environment model

Menetelmiä lasten näkötiedon käsittelyn arvioimiseksi katseenseurannan avulla

Cortical visual processing and mechanism under eye movements and visiospatial attention undergo prominent developmental changes during the first 12 months of infancy. At that time, these key functions of vision are tightly connected to the early brain development in general. Thus, they are favourable targets for new research methods that can be used in treatment, prediction, or detection of various adverse visual of neurocognitive conditions. This thesis presents two eye tracker assisted test paradigms that may be used to evaluate and quantify different functions of infants’ visual processing. The first study concentrates on the analysis of the gaze patterns in classic face-distractor competition paradigm known to tap mechanisms under infant’s attention disengagement and visuospatial orienting. A novel stimuli over a given period of time. In further evaluation, the metric is shown to be sensitive to developmental changes in infants’ face processing between 5 and 7 months of age. The second study focuses on the visual evoked potentials (VEPs) elicited by orientation reversal, global form, and clobal motion stimulation known to measure distinct aspects of visual processing at the cortical level. To improve the reality of such methods, an eye tracker is integrated to the recording setup, which can be used to control stimulus presentation to capture the attention of the infant, and in the analysis to exclude the electroencephalography (EEG) segments with disorientated gaze. With this setup, VEPs can be detected from the vast majority of the tested 3-month-old infants (N=39) using circular variant of Hotelling’s T2 test statistic and two developed power spectrum based metrics. After further development already in progress, the presented methods are ready to be used clinically in assessments of neurocognitive development, preferably alongside other similar biomarker tests of infancy.Näkötiedon käsittely aivokuorella sekä silmänliikkeiden ja visuospatiaalisen tarkkaavaisuuden mekanismit kehittyvät valtavasti lapsen ensimmäisen 12 elinkuukauden kuluessa. Nämä näön avaintoiminnot ovat tiukasti sidoksissa aivojen yleiseen varhaiskehitykseen, jonka vuoksi ne ovat suotuisia kohteita uusille tutkimusmenetelmille käytettäväksi visuaalisen tai neurologisten ongelmien hoidossa, ennustuksessa ja löytämisessä. Tämä työ esittelee kaksi katseenseurantaa hyödyntävää koeasetelmaa, joita voidaan käyttää lasten kortikaalisen näkötiedon käsittelyn arvioinnissa ja kvantifionnissa. Ensimmäisessä tutkimuksessa kehitettiin mitattujen katsekuvioiden analyysiä klassisessa kasvokuva-distraktori-koeasetelmassa, jonka tiedetään koskettavan lasten tarkkavaisuuden vapauttamiseen ja katseen siirtoon liittyviä mekanismeja. Työssä kehitetyllä laskennallisella mittarilla pystytään määrittämään tarkkavaisuuden jakautuminen ruudun keskellä ja raunalla esitettyjen ärsykkeiden välillä haluttuna aikana. Jatkotarkastelu osoittaa mittarin olevan herkkä kasvokuvien käsittelyn kehityksen muutoksille 5 ja 7 kuukauden ikäisten lasten välillä. Toinen osatyö keskittyy näkötiedon kortikaalista käsittelyä heijastavien, suunnan kääntämisen, globaalin muodon tai liikkeen tuottamien näköherätepotentiaalien mittaamiseen ja analyysiin. Parantaakseen menetelmien luotettavuutta laitteistoon liitetään silmänliikekamera, joka mahdollistaa sekä ärsyketoiston ohjaamisen lapsen tarkkaavaisuuden mukaisesti että kerätyn aivosähkökäyrän karsimisen niiltä osin, jolloin lapsen katse oli harhautunut esityksestä. Käyttäen muunnelmaa Hotellingin T2 statistiikasta ja kahta työssä kehitettyä, tehospektriin pohjautuvaa analyysimenetelmää herätevasteet pystytään löytämään valtaosalta 3 kuukauden ikäisistä lapsista (N=39). Meneillään olevan jatkokehityksen jälkeen esitetyt menetelmät ovat valmiita kliiniseen käyttöön neurokognitiivisen kehityksen arvioinnissa muiden vastaavien biomarkkeritutkimuksen rinnalla

Reaching Consensus with uncertainty on a network

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 189-197).As modern communication networks become increasingly advanced, so does the ability and necessity to communicate to make more informed decisions. However, communication alone is not sucient; the method by which information is incorporated and used to make the decision is of critical importance. This thesis develops a novel distributed agreement protocol that allows multiple agents to agree upon a parameter vector particularly when each agent has a unique measure of possibly non-Gaussian uncertainty in its estimate. The proposed hyperpa- rameter consensus algorithm builds upon foundations in both the consensus and data fusion communities by applying Bayesian probability theory to the agreement problem. Unique to this approach is the ability to converge to the centralized Bayesian parameter estimate of non-Gaussian distributed variables over arbitrary, strongly connected networks and without the burden of the often prohibitively complex lters used in traditional data fusion solutions. Convergence properties are demonstrated for local estimates described by a number of common probability distributions and over a range of networks. The benet of the proposed method in distributed estimation is shown through its application to a multi-agent reinforcement learning problem. Additionally, this thesis describes the hardware implementation and testing of a distributed coordinated search, acquisition and track algorithm, which is shown to capably handle the con icting goals of searching and tracking. However, it is sensitive to the estimated target noise characteristics and assumes consistent search maps across the fleet.(cont.) Two improvements are presented to correct these issues: an adaptive tracking algorithm which improves the condence of target re-acquisition in periodic tracking scenarios, and a method to combine disjoint probabilistic search maps using the hyperparameter consensus algorithm to obtain the proper centralized search map.by Cameron S. R. Fraser.S.M