Στην παρούσα διπλωματική εργασία γίνεται μελέτη και ανάλυση της ανθρώπινης κίνησης με σκοπό την αναγνώριση και τον χαρακτηρισμό της. Στο κεφάλαιο 1 παρουσιάζεται το θεωρητικό υπόβαθρο, περιγράφονται εν συντομία τα μέρη της ανάλυσης μιας ολοκληρωμένης διαδικασίας και ορίζονται οι έννοιες οι οποίες θα χρησιμοποιηθούν στην συνέχεια. Στο κεφάλαιο 2 παρουσιάζονται τα μοντέλα και οι τεχνικές που χρησιμοποιούνται κυρίως για την αφαίρεση φόντου σε μια εικόνα και γίνεται υλοποίηση και εφαρμογή, ορισμένων από αυτών, σε βίντεο με συγκεκριμένα χαρακτηριστικά με στόχο την σύγκριση των αποτελεσμάτων. Στο κεφάλαιο 3 παρουσιάζονται οι τεχνικές, και οι κύριοι αντιπρόσωποι αυτών, που χρησιμοποιούνται για την αναγνώριση κινούμενης οντότητας εντός μιας ακολουθίας εικόνων. Στο κεφάλαιο 4 γίνεται υλοποίηση αλγόριθμων, σύμφωνα με τις τεχνικές που αναπτύχτηκαν στο κεφάλαιο 3, και εφαρμογής τους σε βίντεο ώστε να μελετήσουμε τα αποτελέσματα, επίσης παρουσιάζονται οι δυνατότητες του simulink και πως μπορούμε να το χρησιμοποιήσουμε ως εργαλείο για να πετύχουμε ίδια αποτελέσματα με αυτά από την συγγραφή κώδικα σε matlab. Στο τελευταίο κεφάλαιο παρουσιάζονται οι τεχνικές που έχουν χρησιμοποιηθεί στην διεθνή βιβλιογραφία για την αναγνώριση κίνησης και στην συνέχεια γίνεται ανάπτυξη αλγόριθμου που χρησιμοποιεί ως αναγνωριστικό χαρακτηριστικό το κέντρο μάζας της κινούμενης οντότητας και μέσω αυτού προσδιορίζεται η μορφή της κίνησης. / n this thesis study and analysis of human motion for the recognition and characterization of. Chapter 1 presents the theoretical background, outlines the parts of analysis of an integrated process and defines the concepts that will used then. Chapter 2 presents the models and techniques are mainly used to remove a background image and is implementation and enforcement, some of them, in video certain characteristics in order to compare the results. At Chapter 3 presents the techniques, and the main representatives of those who used to identify an entity within a moving sequence of images. Chapter 4 is implementing algorithms under the techniques being developed in Chapter 3, and their application to video To study the results also shows the potential of simulink and how we can use it as a tool to achieve same results with the ones writing code in matlab. In the last chapter presents the techniques used in international literature to identify traffic and then becomes growth algorithm used as an identifier attribute the center of mass the moving entity and this is determined by the shape of motion.

This thesis aims to explore the possibilities of improving the viewer experience of a broadcasted football game with the use of additional information, applied to the footage with the use of motion tracking. This work was conducted in collaboration with SVT Design. which is the inhouse department of the Swedish television network (SVT) tasked with designing, developing and creating graphical solutions. One of the systems developed by SVT Design is the character generator Caspar CG. Which is an open source CG system used worldwide for broadcasting productions. In the spring of 2018, SVT Design presented the idea of incorporating a motion tracking feature within Caspar CG. This would be a feature which could be used during broadcasted sporting events to provide the viewers with additional information regarding the ongoing event. With the use of motion tracking, the additional information could be presented in a dynamic manner in the sense that the information would follow the motion of the tracked object. This thesis aimed to answer the following three research questions; What type of information could be displayed? When and how could this information be displayed? and lastly, how could the addition of information change the viewer’s experience of the football game? The conclusions aimed to provide SVT Design with a set of guidelines and requirements regarding the design and implementation of the additional information in a manner that would promote a positive viewer experience. The methodology applied for this thesis was a qualitative methodology utilizing research activities such as semi-structured interviews featuring three staff members of SVTs department of sport productions. The observation of two broadcasted football games. Along with two focus groups in which the participants were presented with a prototype developed in Adobe After Effects. Consisting of footage from the 2010 FIFA world cup along with additional information that was applied with the use of motion tracking. Through the analysis of the collected data, several recurring keywords and notions were identified and translated into requirements. The requirements, which was structured around the three research questions. Is for example that the information needs to be player specific and to provide an insight of the potential outcome of the game. Another example of a requirement being that the information is to be displayed when there is a break in action during the game. The result from this thesis indicated that if the specified requirements were met. The additional information applied during the broadcast could provide an improvement of the viewer’s experience of watching the broadcasted football game.

The Feldenkrais Method of somatic education has become popular with pianists for improving ease of motion and musculoskeletal health. This thesis contains three studies investigating motion-tracking technologies as means to objectively assess the impact of Feldenkrais training on pianist posture. The first study investigates the accuracy and reliability of Dartfish 2D motion tracking software. Results indicate that Dartfish tracking error is within +/- 0.25 centimeters. The second study uses Dartfish to track head, shoulder, and spine positions of 15 pianists during performance before and after receiving a Feldenkrais Functional Integration Lesson. Comparisons of pre- and post-test measurements indicate no group trends in posture change. However, intriguing changes to movement quality in the head and torso were observable for two participants. The third study compares tracking quality of Dartfish and the Microsoft Kinect for the head, shoulders, and arms of four pianists attending a weeklong Feldenkrais workshop. Results reveal frequent tracking errors with the Kinect sensor, making it unsuitable to measure the impact of somatic training on pianist posture.

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In this thesis, we present a new class of object trackers that are based ona boosted Multiple Instance Learning (MIL) algorithm to track an object in a video sequence. We show how the scope of such trackers can be expanded to the tracking of articulated movements by humans that frequently<br>result in large frame-to-frame variations in the appearance of what needs to be tracked. To deal with the problems caused by such variations, we present a component-based MIL (CMIL) algorithm with boosted learning. The components are the output of an image segmentation algorithm and give the boosted MIL the additional degrees of freedom that it needs in order to deal with the large frame-to-frame variations associated with articulated movements. Furthermore we explored two enhancements of the basic CMIL tracking algorithm. The first is based on an extended definition of positive learning samples for CMIL tracking. This extended definition can filter out false-positive learning samples in order to increase the robustness of CMIL tracking. The second enhancement is based on a combined motion prediction framework with the basic CMIL tracking for resolving issues arising from large and rapid translational human movements. The need for appropriate motion transition can be satisfied by probabilistic modeling of motion. Experimental results show that the proposed approaches yield robust tracking performances in various tracking environments, such as articulate human movements as well as ground human movements observed from aerial vehicles.

<div> <div> <div> <p>Billions of “smart” things in our lives have been equipped with various sensors. Current devices, such as smartphones, smartwatches, tablets, and VR/AR headsets, are equipped with a variety of embedded sensors, e.g. accelerometer, gyroscope, magnetometer, camera, GPS sensor, etc. Based on these sensor data, many technologies have been developed to track human motion at different granularities and to enable new applications. This dissertation examines two challenging problems in human motion tracking. One problem is the ID association issue when utilizing external sensors to simultaneously track multiple people. Although an “outside” system can track all human movements in a designated area, it needs to digitally associate each tracking trajectory to the corresponding person, or say the smart device carried by that person, to provide customized service based on the tracking results. Another problem is the inaccuracy caused by limited sensing information when merely using the embedded sensors located on the devices being tracked. Since sensor data may contain inevitable noises and there is no external beacon used as a reference point for calibration, it is hard to accurately track human motion only with internal sensors.</p><p>In this dissertation, we focus on applying multimodal sensing to perform human motion tracking in mobile systems. To address the two above problems separately, we conduct the following research works. (1) The first work seeks to enable public cameras to send personalized messages to people without knowing their phone addresses. We build a system which utilizes the users’ motion patterns captured by the cameras as their communication addresses, and depends on their smartphones to locally compare the sensor data with the addresses and to accept the correct messages. To protect user privacy, the system requires no data from the users and transforms the motion patterns into low-dimensional codes to prevent motion leaks. (2) To enhance distinguishability and scalability of the camera-to-human communication system, we introduce context features which include both motion patterns and ambience features (e.g. magnetic field, Wi-Fi fingerprint, etc.) to identify people. The enhanced system achieves higher association accuracy and is demonstrated to work with dense people in a retailer, with a fixed-length packet overhead. The first two works explore the potential of widely deployed surveillance cameras and provide a generic underlay to various practical applications, such as automatic audio guide, indoor localization, and sending safety alerts. (3) We close this dissertation with a fine-grained motion tracking system which aims to track the positions of two hand-held motion controllers in a mobile VR system. To achieve high tracking accuracy without external sensors, we introduce new types of information, e.g. ultrasonic ranging among the headset and the controllers, and a kinematic arm model. Effectively fusing this additional information with inertial sensing generates accurate controller positions in real time. Compared with commodity mobile VR controllers which only support rotational tracking, our system provides an interactive VR experience by letting the user actually move the controllers’ positions in a VR scene. To summarize, this dissertation shows that multimodal sensing can further explore the potential power in sensor data and can take sensor-based applications to the next generation of innovation.</p><div><br></div></div></div></div><div><div><div> </div> </div> </div>

Autonomous robot navigation in dynamic environments requires robust detection of egomotion and independent motion. This thesis introduces a novel solution to the problem of visual independent motion detection by interpreting the topological features of omnidirectional dense optical flow field and determining the background - egomotion direction. The thesis solves the problem of visual independent motion detection in four interdependent subtasks. Independent Motion Detection can only be accomplished if the egomotion detection yields a relevant background motion model. Therefore, the problem of Egomotion Detection is solved first by exploiting the topological structures of the global omnidirectional optical flow fields. The estimation of the optical flow field is the prerequisite of the Egomotion-Detection task. Since the omnidirectional projection introduces non-affine deformations on the image plane, the known optical flow calculation methods have to be modified to yield accurate results. This modification is introduced here as another subtask, Omnidirectional Optical Flow Estimation. The experiments concerning the 3D omnidirectional scene capturing are grouped under the fourth subtask 3D Omni-Image Processing.

This thesis contributes to the topic of image-based feature localization and tracking in fluoroscopic (2D x-ray) image sequences. Such tracking is needed to automatically measure organ motion in cancer patients treated with radiation therapy. While the use of 3D cone-beam computed tomography (CBCT) images is a standard clinical practice for verifying the agreement of the patient's position to a plan, it is done before the treatment procedure. Hence, measurement of the motion during the procedure could improve plan design and the accuracy of treatment delivery. Using an existing CBCT imaging system is one way of collecting fluoroscopic sequences for such analysis. Since x-ray images of soft tissues are typically characterized with low contrast and high noise, radio-opaque fiducial markers are often inserted in or around the target. This thesis describes techniques that comprise a complete system for automated detection and tracking of the markers in fluoroscopic image sequences. One of the cornerstone design ideas in this thesis is the use of the 3D CBCT image of the patient, from which the markers can be extracted more easily, to initialize the tracking in the fluoroscopic image sequences. To do this, a specific marker-based image registration framework was proposed. It includes multiple novel techniques, such as marker segmentation and modelling, the marker enhancement filter, and marker-specific template image generation approaches. Through extensive experiments on testing data sets, these novel techniques were combined with appropriate state-of-the-art methods to produce a sleek, computationally efficient, fully automated system that achieved reliable marker localization and tracking. The accuracy of the system is sufficient for clinical implementation. The thesis demonstrates an application of the system to the images of prostate cancer patients, and includes examples of statistical analysis of organ motion that can be used to improve treatment planning. / Dissertation / Doctor of Philosophy (PhD) / This thesis presents the development of a software system that analyzes sequences of 2D x-ray images to automatically measure organ motion in patients undergoing radiation therapy for cancer treatment. The knowledge of motion statistics obtained from this system creates opportunities for patient-specific treatment design that may lead to a better outcome. Automated processing of organ motion is challenging due to the low contrast and high noise levels in the x-ray images. To achieve reliable detection, the proposed system was designed to make use of 3D cone-beam computed tomography images of the patient, where the features (markers) are easier to identify. This required the development of a specific image registration framework for aligning the images, including a number of novel feature modelling and image processing techniques. The proposed motion tracking approach was implemented as a complete software system that was extensively validated on phantom and patient studies. It achieved a level of accuracy and reliability that is suitable for clinical implementation.

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Tracking human hand motions has raised more attention due to the recent advancements of virtual reality (Rheingold, 1991) and prosthesis control (Antfolk et al., 2010). Surface electromyography (sEMG) has been the predominant method for sensing electrical activity in biomechanical studies, and has also been applied to motion tracking in recent years. While most studies focus on the classification of human hand motions within a predefined motion set, the prediction of continuous finger joint angles and wrist angles remains a challenging endeavor. In this research, a biomechanical knowledge-driven data fusion strategy is proposed to predict finger joint angles and wrist angles. This strategy combines time series data of sEMG signals and simulated muscle features, which can be extracted from a biomechanical model available in OpenSim (Delp et al., 2007). A support vector regression (SVR) model is used to firstly predict muscle features from sEMG signals and then to predict joint angles from the estimated muscle features. A set of motion data containing 10 types of motions from 12 participants was collected from an institutional review board approved experiment. A hypothesis was tested to validate whether adding the simulated muscle features would significantly improve the prediction performance. The study indicates that the biomechanical knowledge-driven data fusion strategy will improve the prediction of new types of human hand motions. The results indicate that the proposed strategy significantly outperforms the benchmark date-driven model especially when the users were performing unknown types of motions from the model training stage. The proposed model provides a possible approach to integrate the simulation models and data fusion models in human factors and ergonomics. / Master of Science / Hand motion tracking is a promising technique for the development of virtual reality and prosthesis. Identifying hand motions based on sensor data is the fundamental step to realize motion tracking. Among all the tracking techniques, surface electromyography (sEMG) is a type of electrical signals that has been proven useful in predicting hand motions in recent years, since sEMG signals can directly reflect muscle activities, and hand motions are controlled by muscle groups. While most studies focus on the classification of human hand motions within a predefined motion set, the prediction of continuous finger joint angles and wrist angles remains a challenging endeavor. In this research, a biomechanical knowledge-driven data fusion strategy was proposed to predict finger joint angles and wrist angles. More specifically, this strategy combined a statistical model with a biomechanical simulation model, and a hypothesis was tested to validate whether adding the biomechanical simulation model would significantly improve the prediction performance. A set of sEMG signals containing 10 types of motions from 12 participants was collected from an institutional review board approved experiment, in order to test the proposed strategy. The results indicate that the proposed strategy significantly outperforms the benchmark statistical models especially when users were performing unknown types of motions from the model training stage. The proposed strategy provides a possible approach to integrate the simulation models and data-driven models in human factors and ergonomics.