Monocular 6DoF pose tracking has many applications in augmented reality, robotics and other areas and because of the rise of deep learning new approaches such as category-level models are successful. The temporal information in sequential data is essential for both online and offline tasks, which can help boost the quality of predictions while encountering some unexpected influences like occlusions and vibration. In 2D object detection and tracking, substantial research has been done in leveraging temporal information to improve the performance of the model. Nevertheless, it is challenging to lift the temporal processing to 3D space because of the ambiguity of the visual data. In this thesis, we propose a method to calculate the temporal difference of points and pixels assuming that the K nearest points share similar features. The extracted features from the difference are learned to weigh the relevant points in the temporal sequence and aggregate them to provide support to the current frame's prediction. We propose a novel difference-based temporal module to incorporate both RGB and 3D points data in a temporal sequence. This module can be easily integrated with any category-level 6DoF pose tracking model which uses RGB and 3D points as input. We evaluate this module on two state-of-the-art category-level 6D pose tracking models and the result shows that it can increase the model's accuracy and robustness in complex scenarios.

Accurate 3D tracking of animals from video recordings is critical for many behavioral studies. However, other than for humans, there is a lack of publicly available datasets of videos of animals that the computer vision community could use for model development. Furthermore, due to occlusion and the uncontrollable nature of the animals, existing pose estimation models suffer from inadequate precision. People rely on biomechanical expertise to design mathematical models to optimize poses to mitigate this issue at the cost of generalization. We propose OptiPose, a generalizable attention-based deep learning pose optimization model, as a part of a post-processing pipeline for refining 3D poses estimated by pre-existing systems. Our experiments show how OptiPose is highly robust to noise and occlusion and can be used to optimize pose sequences provided by state-of-the-art models for animal pose estimation. Furthermore, we will make Rodent3D, a multimodal (RGB, Thermal, and Depth) dataset for rats, publicly available.

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Modern facial feature tracking techniques can automatically extract and accurately track multiple facial landmark points from faces in video streams in real time. Facial landmark points are defined as points distributed on a face in regards to certain facial features, such as eye corners and face contour. This opens up for using facial feature movements as a handsfree human-computer interaction technique. These alternatives to traditional input devices can give a more interesting gaming experience. They also open up for more intuitive controls and can possibly give greater access to computers and video game consoles for certain disabled users with difficulties using their arms and/or fingers. This research explores using facial feature tracking to control a character's movements in a platform game. The aim is to interpret facial feature tracker data and convert facial feature movements to game input controls. The facial feature input is compared with other handsfree inputmethods, as well as traditional keyboard input. The other handsfree input methods that are explored are head pose estimation and a hybrid between the facial feature and head pose estimation input. Head pose estimation is a method where the application is extracting the angles in which the user's head is tilted. The hybrid input method utilises both head pose estimation and facial feature tracking. The input methods are evaluated by user performance and subjective ratings from voluntary participants playing a platform game using the input methods. Performance is measured by the time, the amount of jumps and the amount of turns it takes for a user to complete a platform level. Jumping is an essential part of platform games. To reach the goal, the player has to jump between platforms. An inefficient input method might make this a difficult task. Turning is the action of changing the direction of the player character from facing left to facing right or vice versa. This measurement is intended to pick up difficulties in controling the character's movements. If the player makes many turns, it is an indication that it is difficult to use the input method to control the character movements efficiently. The results suggest that keyboard input is the most effective input method, while it is also the least entertaining of the input methods. There is no significant difference in performance between facial feature input and head pose input. The hybrid input version has the best results overall of the alternative input methods. The hybrid input method got significantly better performance results than the head pose input and facial feature input methods, while it got results that were of no statistically significant difference from the keyboard input method. Keywords: Computer Vision, Facial Feature Tracking, Head Pose Tracking, Game Control / Moderna tekniker kan automatiskt extrahera och korrekt följa multipla landmärken från ansikten i videoströmmar. Landmärken från ansikten är definerat som punkter placerade på ansiktet utefter ansiktsdrag som till exempel ögat eller ansikts konturer. Detta öppnar upp för att använda ansiktsdragsrörelser som en teknik för handsfree människa-datorinteraktion. Dessa alternativ till traditionella tangentbord och spelkontroller kan användas för att göra datorer och spelkonsoler mer tillgängliga för vissa rörelsehindrade användare. Detta examensarbete utforskar användbarheten av ansiktsdragsföljning för att kontrollera en karaktär i ett plattformsspel. Målet är att tolka data från en appliktion som följer ansiktsdrag och översätta ansiktsdragens rörelser till handkontrollsinmatning. Ansiktsdragsinmatningen jämförs med inmatning med huvudposeuppskattning, en hybrid mellan ansikstdragsföljning och huvudposeuppskattning, samt traditionella tangentbordskontroller. Huvudposeuppskattning är en teknik där applikationen extraherar de vinklar användarens huvud lutar. Hybridmetoden använder både ansiktsdragsföljning och huvudposeuppskattning. Inmatningsmetoderna granskas genom att mäta effektivitet i form av tid, antal hopp och antal vändningar samt subjektiva värderingar av frivilliga testanvändare som spelar ett plattformspel med de olika inmatningsmetoderna. Att hoppa är viktigt i ett plattformsspel. För att nå målet, måste spelaren hoppa mellan plattformar. En inefektiv inmatningsmetod kan göra detta svårt. En vändning är när spelarkaraktären byter riktning från att rikta sig åt höger till att rikta sig åt vänster och vice versa. Ett högt antal vändningar kan tyda på att det är svårt att kontrollera spelarkaraktärens rörelser på ett effektivt sätt. Resultaten tyder på att tangentbordsinmatning är den mest effektiva metoden för att kontrollera plattformsspel. Samtidigt fick metoden lägst resultat gällande hur roligt användaren hade under spelets gång. Där var ingen statisktiskt signifikant skillnad mellan huvudposeinmatning och ansikstsdragsinmatning. Hybriden mellan ansiktsdragsinmatning och huvudposeinmatning fick bäst helhetsresultat av de alternativa inmatningsmetoderna. Nyckelord: Datorseende, Följning av Ansiktsdrag, Följning av Huvud, Spelinmatning

This thesis presents a framework for a hybrid model-free marker-less inertial-visual camera pose tracking with an integrated sensor fusion mechanism. The proposed solution addresses the fundamental problem of pose recovery in computer vision and robotics and provides an improved solution for wide-area pose tracking that can be used on mobile platforms and in real-time applications. In order to arrive at a suitable pose tracking algorithm, an in-depth investigation was conducted into current methods and sensors used for pose tracking. Preliminary experiments were then carried out on hybrid GPS-Visual as well as wireless micro-location tracking in order to evaluate their suitability for camera tracking in wide-area or GPS-denied environments. As a result of this investigation a combination of an inertial measurement unit and a camera was chosen as the primary sensory inputs for a hybrid camera tracking system. After following a thorough modelling and mathematical formulation process, a novel and improved hybrid tracking framework was designed, developed and evaluated. The resulting system incorporates an inertial system, a vision-based system and a recursive particle filtering-based stochastic data fusion and state estimation algorithm. The core of the algorithm is a state-space model for motion kinematics which, combined with the principles of multi-view camera geometry and the properties of optical flow and focus of expansion, form the main components of the proposed framework. The proposed solution incorporates a monitoring system, which decides on the best method of tracking at any given time based on the reliability of the fresh vision data provided by the vision-based system, and automatically switches between visual and inertial tracking as and when necessary. The system also includes a novel and effective self-adjusting mechanism, which detects when the newly captured sensory data can be reliably used to correct the past pose estimates. The corrected state is then propagated through to the current time in order to prevent sudden pose estimation errors manifesting as a permanent drift in the tracking output. Following the design stage, the complete system was fully developed and then evaluated using both synthetic and real data. The outcome shows an improved performance compared to existing techniques, such as PTAM and SLAM. The low computational cost of the algorithm enables its application on mobile devices, while the integrated self-monitoring, self-adjusting mechanisms allow for its potential use in wide-area tracking applications.

QC 20100621

Diplomová práce je zaměřena na problematiku detekce a sledování polohy hlavy v obraze jako jednu s možností jak zlepšit možnosti interakce mezi počítačem a člověkem. Hlavním přínosem diplomové práce je využití inovativních hardwarových a softwarových technologií jakými jsou Microsoft Kinect, Point Cloud Library a CImg Library. Na úvod je představeno shrnutí předchozích prací na podobné téma. Následuje charakteristika a popis databáze, která byla vytvořena pro účely diplomové práce. Vyvinutý systém pro detekci a sledování polohy hlavy je založený na akvizici 3D obrazových dat a registračním algoritmu Iterative Closest Point. V závěru diplomové práce je nabídnuto hodnocení vzniklého systému a jsou navrženy možnosti jeho budoucího zlepšení.