Gravity optimised particle filter for hand tracking

This paper presents a gravity optimised particle filter (GOPF) where the magnitude of the gravitational force for every particle is proportional to its weight. GOPF attracts nearby particles and replicates new particles as if moving the particles towards the peak of the likelihood distribution, improving the sampling efficiency. GOPF is incorporated into a technique for hand features tracking. A fast approach to hand features detection and labelling using convexity defects is also presented. Experimental results show that GOPF outperforms the standard particle filter and its variants, as well as state-of-the-art CamShift guided particle filter using a significantly reduced number of particles

Vision-Aided Navigation for GPS-Denied Environments Using Landmark Feature Identification

In recent years, unmanned autonomous vehicles have been used in diverse applications because of their multifaceted capabilities. In most cases, the navigation systems for these vehicles are dependent on Global Positioning System (GPS) technology. Many applications of interest, however, entail operations in environments in which GPS is intermittent or completely denied. These applications include operations in complex urban or indoor environments as well as missions in adversarial environments where GPS might be denied using jamming technology. This thesis investigate the development of vision-aided navigation algorithms that utilize processed images from a monocular camera as an alternative to GPS. The vision-aided navigation approach explored in this thesis entails defining a set of inertial landmarks, the locations of which are known within the environment, and employing image processing algorithms to detect these landmarks in image frames collected from an onboard monocular camera. These vision-based landmark measurements effectively serve as surrogate GPS measurements that can be incorporated into a navigation filter. Several image processing algorithms were considered for landmark detection and this thesis focuses in particular on two approaches: the continuous adaptive mean shift (CAMSHIFT) algorithm and the adaptable compressive (ADCOM) tracking algorithm. These algorithms are discussed in detail and applied for the detection and tracking of landmarks in monocular camera images. Navigation filters are then designed that employ sensor fusion of accelerometer and rate gyro data from an inertial measurement unit (IMU) with vision-based measurements of the centroids of one or more landmarks in the scene. These filters are tested in simulated navigation scenarios subject to varying levels of sensor and measurement noise and varying number of landmarks. Finally, conclusions and recommendations are provided regarding the implementation of this vision-aided navigation approach for autonomous vehicle navigation systems

Comparison between gaze and moving objects in videos for smooth pursuit eye movement evaluation

When viewing moving objects in videos the movement of the eyes is called smooth pursuit. For evaluating the relationship of eye tracking data to the moving objects, the objects in the videos need to be detected and tracked. In the first part of this thesis, a method for detecting and tracking of moving objects in videos is developed. The method mainly consists of a modified version of the Gaussian mixture model, The Tracking feature point method, a modified version of the Mean shift algorithm, Matlabs function bwlabel and a set of new developed methods. The performance of the method is highest when the background is static and the objects differ in colour from the background. The false detection rate increases, when the video environment becomes more dynamic and complex. In the second part of this thesis the distance between the point of gaze and the moving objects centre point is calculated. The eyes may not always follow the centre position of an object, but rather some other part of the object. Therefore, the method gives more satisfactory result when the objects are small.Utvärdering av smooth pursuit-rörelser. En jämförelse mellan ögonrörelser och rörliga objekt i videosekvenser Populärvetenskaplig sammanfattning av examensarbetet: Andrea Åkerström Ett forskningsområde som har vuxit mycket de senaste åren är ”eye tracking”: en teknik för att undersöka ögonrörelser. Tekniken har visat sig intressant för studier inom exempelvis visuella system, i psykologi och i interaktioner mellan datorer och människor. Ett eye tracking system mäter ögonens rörelser så att de punkterna ögat tittar på kan bli estimerade. Tidigare har de flesta studier inom eye tracking baserats på bilder, men på senare tid har även intresset för att studera filmsekvenser vuxit. Den typ av rörelse som ögat utför när det följer ett rörligt objekt kallas för smooth pursuitrörelse. En av svårigheterna med att utvärdera relationen mellan eye tracking-data och rörliga objekten i filmer är att objekten, antingen manuellt mäts ut eller att ett intelligent system utvecklas för en automatisk utvärdering. Det som gör processen att detektera och följa rörliga objekt i filmer komplex är att olika videosekvenser kan ha många olika typer av svåra videoscenarion som metoden måste klara av. Till exempel kan bakgrunden i en video vara dynamisk, det kan finnas störningar som regn eller snö, eller kan problemet vara att kameran skakar eller rör sig. Syftet med detta arbete består av två delar. Den först delen, som också har varit den största, har varit att utveckla en metod som kan detektera och följa rörliga objekt i olika typer av videosekvenser, baserad på metoder från tidigare forskning. Den andra delen har varit att försöka utveckla en automatisk utvärdering av ögonrörelsen smooth persuit, genom att använda de detekterade och följda objekten i videosekvenserna tillsammans med redan existerande ögondata. För att utveckla den metod har olika metoder från tidigare forskning kombinerat. Alla metoder som har utvecklas i detta område har olika för och nackdelar och fungerade bättre eller sämre för olika typer av videoscenarion. Målet för metoden i detta arbete har varit att hitta en kombination av olika metoder som, genom att kompensera varandras för- och nackdelar, kan ge en så bra detektering som möjligt för olika typer av filmsekvenser. Min metod är till största del uppbyggd av tre metoder: En modifierad version av Guasssian Mixture Model, Tracking Feature Point och en modifierad version av Mean Shift Algorithmen. Guassian Mixture Model-metoden används för att detekterar pixlar i filmen som tillhör objekt som är i rörelse. Metoden tar fram dynamiska modeller av bakgrunden i filmen och detekterar pixlar som skiljer sig från backgrundsmodellerna. Detta är en väl använd metod som kan hantera komplexa bakgrunder med periodiskt brus, men den ger samtidigt ofta upphov till felaktiga detektioner och den kan inte hantera kamerarörelser. För att hantera kamerarörelser används Tracking Feature Point-metoden och på så sätt kompenseras denna brist hos Guassian Mixture Modell-metoden. Tracking Feature Point tar fram ”feature points” ut videobilder och med hjälp av dem kan metoden estimera kameraförflyttningar. Denna metod räknar dock endast ut de förflyttningar som kameran gör, men den tar inte hänsyn till om kameran roterar. Mean Shift Algoritm är en metod som används för att räkna ut det rörliga objektets nya position i en efterföljande bild. För mitt arbete har endast delar av denna metod används till att bestämma vilka detektioner av objekt i de olika bilderna som representerar samma objekt. Genom att ta fram modeller för objekten i varje bild, vilka sedan jämförs, kan metoden bestämma vilka objekt som kan klassas som samma objekt. Den metod som har utvecklat i detta arbete gav bäst resultat när bakgrunden var statisk och objektets färg skiljde sig från bakgrunden. När bakgrunden blir mer dynamisk och komplex ökade mängden falska detektioner och för vissa videosekvenser misslyckas metoden att detektera hela objekten. Den andra delen av detta arbetes syfte var att använda resultatet från metoden för att utvärdera eye tracking-data. Den automatiska utvärderingen av ögonrörelsen smooth pursuit ger ett mått på hur bra ögat kan följa objekt som rör sig. För att utföra detta mäts avståndet mellan den punkt som ögat tittar på och det detekterade objektets centrum. Den automatiskt utvärderingen av smooth pursuit-rörelsen gav bäst resultat när objekten var små. För större objekt följer ögat inte nödvändigtvis objektets mittenpunkt utan istället någon annan del av objektet och metoden kan därför i dessa fall ge ett missvisande resultat. Detta arbete har inte resulterat i en färdig metod utan det finns många områden för förbättringar. Exempelvis skulle en estimering av kamerans rotationer förbättra resultaten. Utvärderingen av hur väl ögat följer rörliga objekt kan även utvecklas mer, genom att konturerna av objekten beräknades. På detta sätt skulle även avståndet mellan punkterna ögat tittar på och objektets area kunnat bestämmas. Både eye tracking och att detektera och följa rörliga objekt i filmer är idag aktiva forskningsområden och det finns alltså fortfarande mycket att utveckla i dessa områden. Syfte med detta arbete har varit att försöka utveckla en mer generell metod som kan fungera för olika typer av filmsekvenser

PPF - A Parallel Particle Filtering Library

We present the parallel particle filtering (PPF) software library, which enables hybrid shared-memory/distributed-memory parallelization of particle filtering (PF) algorithms combining the Message Passing Interface (MPI) with multithreading for multi-level parallelism. The library is implemented in Java and relies on OpenMPI's Java bindings for inter-process communication. It includes dynamic load balancing, multi-thread balancing, and several algorithmic improvements for PF, such as input-space domain decomposition. The PPF library hides the difficulties of efficient parallel programming of PF algorithms and provides application developers with the necessary tools for parallel implementation of PF methods. We demonstrate the capabilities of the PPF library using two distributed PF algorithms in two scenarios with different numbers of particles. The PPF library runs a 38 million particle problem, corresponding to more than 1.86 GB of particle data, on 192 cores with 67% parallel efficiency. To the best of our knowledge, the PPF library is the first open-source software that offers a parallel framework for PF applications.Comment: 8 pages, 8 figures; will appear in the proceedings of the IET Data Fusion & Target Tracking Conference 201

Non-invasive respiration monitoring by thermal imaging to detect sleep apnoea

Accurate airflow measurements are vital to diagnose apnoeas; respiratory pauses occurring during sleep that interrupt airflow to the lungs. Apnoea diagnosis usually requires an overnight polysomnography during which numerous vital signs are monitored, including respiratory rate and airflow. The current gold standard in respiration monitoring is a nasal pressure sensor which is placed inside the nostrils of the patient and through which the airflow is measured. Due to the contact nature of the sensor, it is often refused or removed during polysomnography, especially in the case of paediatric patients. We have found that around 50% of children refuse the use of nasal prongs due to its in-vasiveness, and of those that accepted it, 64% removed the sensor over the course of the polysomnography. We evaluated a non-contact method to monitor respiration by developing infrared thermal imaging, whereby temperature fluc-tuations associated with respiration are measured and correlated with airflow. A study was carried out on a sample of 11 healthy adult volunteers whose res-piratory signals were recorded over four simulated apnoea scenarios. The res-piratory signal obtained through thermal imaging was compared against the gold standard nasal pressure sensor. In 70% of cases, apnoea related events were well correlated with airflow sensor readings. In 16% of recordings the subject’s head position did not allow correct identification of the region of interest (i.e. the nostrils). For the remaining 14% of cases there was partial agreement between the thermal measurements and airflow sensor readings. These results indicate thermal imaging can be valuable as a detection tool for sleep apnoea, particularly in the case of paediatric patients

Three hypothesis algorithm with occlusion reasoning for multiple people tracking

This work proposes a detection-based tracking algorithm able to locate and keep the identity of multiple people, who may be occluded, in uncontrolled stationary environments. Our algorithm builds a tracking graph that models spatio-temporal relationships among attributes of interacting people to predict and resolve partial and total occlusions. When a total occlusion occurs, the algorithm generates various hypotheses about the location of the occluded person considering three cases: (a) the person keeps the same direction and speed, (b) the person follows the direction and speed of the occluder, and (c) the person remains motionless during occlusion. By analyzing the graph, our algorithm can detect trajectories produced by false alarms and estimate the location of missing or occluded people. Our algorithm performs acceptably under complex conditions, such as partial visibility of individuals getting inside or outside the scene, continuous interactions and occlusions among people, wrong or missing information on the detection of persons, as well as variation of the person’s appearance due to illumination changes and background-clutter distracters. Our algorithm was evaluated on test sequences in the field of intelligent surveillance achieving an overall precision of 93%. Results show that our tracking algorithm outperforms even trajectory-based state-of-the-art algorithms

Object Tracking with Adaptive Multicue Incremental Visual Tracker

Generally, subspace learning based methods such as the Incremental Visual Tracker (IVT) have been shown to be quite effective for visual tracking problem. However, it may fail to follow the target when it undergoes drastic pose or illumination changes. In this work, we present a novel tracker to enhance the IVT algorithm by employing a multicue based adaptive appearance model. First, we carry out the integration of cues both in feature space and in geometric space. Second, the integration directly depends on the dynamically-changing reliabilities of visual cues. These two aspects of our method allow the tracker to easily adapt itself to the changes in the context and accordingly improve the tracking accuracy by resolving the ambiguities. Experimental results demonstrate that subspace-based tracking is strongly improved by exploiting the multiple cues through the proposed algorithm

Real-Time, Multiple Pan/Tilt/Zoom Computer Vision Tracking and 3D Positioning System for Unmanned Aerial System Metrology

The study of structural characteristics of Unmanned Aerial Systems (UASs) continues to be an important field of research for developing state of the art nano/micro systems. Development of a metrology system using computer vision (CV) tracking and 3D point extraction would provide an avenue for making these theoretical developments. This work provides a portable, scalable system capable of real-time tracking, zooming, and 3D position estimation of a UAS using multiple cameras. Current state-of-the-art photogrammetry systems use retro-reflective markers or single point lasers to obtain object poses and/or positions over time. Using a CV pan/tilt/zoom (PTZ) system has the potential to circumvent their limitations. The system developed in this paper exploits parallel-processing and the GPU for CV-tracking, using optical flow and known camera motion, in order to capture a moving object using two PTU cameras. The parallel-processing technique developed in this work is versatile, allowing the ability to test other CV methods with a PTZ system using known camera motion. Utilizing known camera poses, the object\u27s 3D position is estimated and focal lengths are estimated for filling the image to a desired amount. This system is tested against truth data obtained using an industrial system