Spatial and Temporal Modeling for Human Activity Recognition from Multimodal Sequential Data

Human Activity Recognition (HAR) has been an intense research area for more than a decade. Different sensors, ranging from 2D and 3D cameras to accelerometers, gyroscopes, and magnetometers, have been employed to generate multimodal signals to detect various human activities. With the advancement of sensing technology and the popularity of mobile devices, depth cameras and wearable devices, such as Microsoft Kinect and smart wristbands, open a unprecedented opportunity to solve the challenging HAR problem by learning expressive representations from the multimodal signals recording huge amounts of daily activities which comprise a rich set of categories. Although competitive performance has been reported, existing methods focus on the statistical or spatial representation of the human activity sequence; while the internal temporal dynamics of the human activity sequence are not sufficiently exploited. As a result, they often face the challenge of recognizing visually similar activities composed of dynamic patterns in different temporal order. In addition, many model-driven methods based on sophisticated features and carefully-designed classifiers are computationally demanding and unable to scale to a large dataset. In this dissertation, we propose to address these challenges from three different perspectives; namely, 3D spatial relationship modeling, dynamic temporal quantization, and temporal order encoding. We propose a novel octree-based algorithm for computing the 3D spatial relationships between objects from a 3D point cloud captured by a Kinect sensor. A set of 26 3D spatial directions are defined to describe the spatial relationship of an object with respect to a reference object. These 3D directions are implemented as a set of spatial operators, such as AboveSouthEast and BelowNorthWest, of an event query language to query human activities in an indoor environment; for example, A person walks in the hallway from north to south. The performance is quantitatively evaluated in a public RGBD object dataset and qualitatively investigated in a live video computing platform. In order to address the challenge of temporal modeling in human action recognition, we introduce the dynamic temporal quantization, a clustering-like algorithm to quantize human action sequences of varied lengths into fixed-size quantized vectors. A two-step optimization algorithm is proposed to jointly optimize the quantization of the original sequence. In the aggregation step, frames falling into the sample segment are aggregated by max-polling and produce the quantized representation of the segment. During the assignment step, frame-segment assignment is updated according to dynamic time warping, while the temporal order of the entire sequence is preserved. The proposed technique is evaluated on three public 3D human action datasets and achieves state-of-the-art performance. Finally, we propose a novel temporal order encoding approach that models the temporal dynamics of the sequential data for human activity recognition. The algorithm encodes the temporal order of the latent patterns extracted by the subspace projection and generates a highly compact First-Take-All (FTA) feature vector representing the entire sequential data. An optimization algorithm is further introduced to learn the optimized projections in order to increase the discriminative power of the FTA feature. The compactness of the FTA feature makes it extremely efficient for human activity recognition with nearest neighbor search based on Hamming distance. Experimental results on two public human activity datasets demonstrate the advantages of the FTA feature over state-of-the-art methods in both accuracy and efficiency

Interactive feature detection in volumetric data

Im Rahmen dieser Dissertation wurden drei Techniken für die interaktive Merkmalsdetektion in Volumendaten entwickelt. Das erste Verfahren auf Basis des LH-Transferfunktionsraumes ermöglicht es dem Benutzer, Objekt-Oberflächen in einem Volumendatensatz durch direktes Markieren im gerenderten Bild zu identifizieren, wobei keine Interaktion im Datenraum des Volumens benötigt wird. Zweitens wird ein formbasiertes Klassifikationsverfahren vorgestellt, das ausgehend von einer groben Vorsegmentierung den Volumendatensatz in eine Menge von kleineren Regionen zerlegt, deren Form anschließend mit eigens entwickelten Klassifikatoren bestimmt wird. Drittens wird ein interaktives Volumen-Segmentierungsverfahren auf Basis des Random Walker-Algorithmus beschrieben, das speziell auf die Verringerung von Fehlklassifizierungen in der resultierenden Segmentierung abzielt. This dissertation presents three volumetric feature detection approaches that focus on an efficient interplay between user and system. The first technique exploits the LH transfer function space in order to enable the user to classify boundaries by directly marking them in the volume rendering image, without requiring interaction in the data domain. Second, we propose a shape-based feature detection approach that blurs the border between fast but limited classification and powerful but laborious segmentation techniques. Third, we present a guided probabilistic volume segmentation workflow that focuses on the minimization of uncertainty in the resulting segmentation. In an iterative process, the system continuously assesses uncertainty of an intermediate random walker-based segmentation in order to detect regions with high ambiguity, to which the user’s attention is directed to support the correction of potential segmentation errors

Volumetric cloud generation using a Chinese brush calligraphy style

Includes bibliographical references.Clouds are an important feature of any real or simulated environment in which the sky is visible. Their amorphous, ever-changing and illuminated features make the sky vivid and beautiful. However, these features increase both the complexity of real time rendering and modelling. It is difficult to design and build volumetric clouds in an easy and intuitive way, particularly if the interface is intended for artists rather than programmers. We propose a novel modelling system motivated by an ancient painting style, Chinese Landscape Painting, to address this problem. With the use of only one brush and one colour, an artist can paint a vivid and detailed landscape efficiently. In this research, we develop three emulations of a Chinese brush: a skeleton-based brush, a 2D texture footprint and a dynamic 3D footprint, all driven by the motion and pressure of a stylus pen. We propose a hybrid mapping to generate both the body and surface of volumetric clouds from the brush footprints. Our interface integrates these components along with 3D canvas control and GPU-based volumetric rendering into an interactive cloud modelling system. Our cloud modelling system is able to create various types of clouds occurring in nature. User tests indicate that our brush calligraphy approach is preferred to conventional volumetric cloud modelling and that it produces convincing 3D cloud formations in an intuitive and interactive fashion. While traditional modelling systems focus on surface generation of 3D objects, our brush calligraphy technique constructs the interior structure. This forms the basis of a new modelling style for objects with amorphous shape

An Unified Multiscale Framework for Planar, Surface, and Curve Skeletonization

Computing skeletons of 2D shapes, and medial surface and curve skeletons of 3D shapes, is a challenging task. In particular, there is no unified framework that detects all types of skeletons using a single model, and also produces a multiscale representation which allows to progressively simplify, or regularize, all skeleton types. In this paper, we present such a framework. We model skeleton detection and regularization by a conservative mass transport process from a shape's boundary to its surface skeleton, next to its curve skeleton, and finally to the shape center. The resulting density field can be thresholded to obtain a multiscale representation of progressively simplified surface, or curve, skeletons. We detail a numerical implementation of our framework which is demonstrably stable and has high computational efficiency. We demonstrate our framework on several complex 2D and 3D shapes