Advances in Monocular Exemplar-based Human Body Pose Analysis: Modeling, Detection and Tracking

Esta tesis contribuye en el análisis de la postura del cuerpo humano a partir de secuencias de imágenes adquiridas con una sola cámara. Esta temática presenta un amplio rango de potenciales aplicaciones en video-vigilancia, video-juegos o aplicaciones biomédicas. Las técnicas basadas en patrones han tenido éxito, sin embargo, su precisión depende de la similitud del punto de vista de la cámara y de las propiedades de la escena entre las imágenes de entrenamiento y las de prueba. Teniendo en cuenta un conjunto de datos de entrenamiento capturado mediante un número reducido de cámaras fijas, paralelas al suelo, se han identificado y analizado tres escenarios posibles con creciente nivel de dificultad: 1) una cámara estática paralela al suelo, 2) una cámara de vigilancia fija con un ángulo de visión considerablemente diferente, y 3) una secuencia de video capturada con una cámara en movimiento o simplemente una sola imagen estática

Human Motion Analysis: From Gait Modeling to Shape Representation and Pose Estimation

This dissertation presents a series of fundamental approaches to the human motion analysis from three perspectives, i.e., manifold learning-based gait motion modeling, articulated shape representation and efficient pose estimation. Firstly, a new joint gait-pose manifold (JGPM) learning algorithm is proposed to jointly optimize the gait and pose variables simultaneously. To enhance the representability and flexibility for complex motion modeling, we also propose a multi-layer JGPM that is capable of dealing with a variety of walking styles and various strides. We resort to a topologically-constrained Gaussian process latent variable model (GPLVM) to learn the multi-layer JGPM where two new techniques are introduced to facilitate model learning. First is training data diversification that creates a set of simulated motion data with different strides under limited data. Second is the topology-aware local learning that is to speed up model learning by taking advantage of the local topological structure. We demonstrate the effectiveness of our approach by synthesizing the high-quality motions from the multi-layer model. The experimental results show that the multi-layer JGPM outperforms several existing GPLVM-based models in terms of the overall performance of motion modeling.On the other hand, to achieve efficient human pose estimation from a single depth sensor, we develop a generalized Gaussian kernel correlation (GKC)-based framework which supports not only body shape modeling, but also articulated pose tracking. We first generalize GKC from the univariate Gaussian to the multivariate one and derive a unified GKC function that provides a continuous and differentiable similarity measure between a template and an observation, both of which are represented by a collection of univariate and/or multivariate Gaussian kernels. Then, to facilitate the data matching and accommodate articulated body deformation, we embed a quaternion-based articulated skeleton into a collection of multivariate Gaussians-based template model and develop an articulated GKC (AGKC) which supports subject-specific shape modeling and articulated pose tracking for both the full-body and hand. Our tracking algorithm is simple yet effective and computationally efficient. We evaluate our algorithm on two benchmark depth datasets. The experimental results are promising and competitive when compared with state-of-the-art algorithms.Electrical Engineerin

06241 Abstracts Collection -- Human Motion - Understanding, Modeling, Capture and Animation. 13th Workshop

From 11.06.06 to 16.06.06, the Dagstuhl Seminar 06241 ``Human Motion - Understanding, Modeling, Capture and Animation. 13th Workshop "Theoretical Foundations of Computer Vision"\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general

Vision-based techniques for gait recognition

Global security concerns have raised a proliferation of video surveillance devices. Intelligent surveillance systems seek to discover possible threats automatically and raise alerts. Being able to identify the surveyed object can help determine its threat level. The current generation of devices provide digital video data to be analysed for time varying features to assist in the identification process. Commonly, people queue up to access a facility and approach a video camera in full frontal view. In this environment, a variety of biometrics are available - for example, gait which includes temporal features like stride period. Gait can be measured unobtrusively at a distance. The video data will also include face features, which are short-range biometrics. In this way, one can combine biometrics naturally using one set of data. In this paper we survey current techniques of gait recognition and modelling with the environment in which the research was conducted. We also discuss in detail the issues arising from deriving gait data, such as perspective and occlusion effects, together with the associated computer vision challenges of reliable tracking of human movement. Then, after highlighting these issues and challenges related to gait processing, we proceed to discuss the frameworks combining gait with other biometrics. We then provide motivations for a novel paradigm in biometrics-based human recognition, i.e. the use of the fronto-normal view of gait as a far-range biometrics combined with biometrics operating at a near distance