There is biological evidence that human actions are composed out of action primitives, like words and sentences being composed out of phonemes. Similarly to language processing, one possibility to model and recognize complex actions is to use grammars with action primitives as the alphabet. A major challenge here is that the action primitives need to be recovered first from the noisy input signal

In this paper, we analyze the different approaches taken to date within the computer vision, robotics and artificial intelligence communities for the representation, recognition, synthesis and understanding of action. We deal with action at different levels of complexity and provide the reader with the necessary related literature references. We put the literature references further into context a

The ability to integrate information over time in order to come to a conclusion is a strength of cognitive systems. It allows the system, e.g., to1verify insecure observations: This is the case when data is noisy or of low-quality, or if conditions in general are non-optimal. 2exploit general knowledge about spatio-temporal relations: This allows the system to exploit the specific dynamics of an o

There is biological evidence that human actions are composed out of action primitives, similarly to words and sentences being composed out of phonemes. Given a set of action primitives and an action composed out of these primitives we present a Hidden Markov Model-based approach that allows to recover the action primitives in that action. In our approach, the primitives may have different lengths,

This survey reviews advances in human motion capture and analysis from 2000 to 2006, following a previous survey of papers up to 2000 [T.B. Moeslund, E. Granum, A survey of computer vision-based human motion capture, Computer Vision and Image Understanding, 81(3) (2001) 231-268.]. Human motion capture continues to be an increasingly active research area in computer vision with over 350 publication

In this paper we introduce a model-based background subtraction approach where first silhouettes, which model the correlations between neightboring pixels are being learned and where then Bayesian propagation over time is used to select the proper silhouette model and tracking parameters. Bayes propagation is attractive in our application as it allows to deal with uncertainties in the video data d

We propose a view-based approach to recognize humans from their gait. Two different image features have been considered: The width of the outer contour of the binarized silhouette of the walking person and the entire binary silhouette itself. To obtain the observation vector from the image features, we employ two different methods. In the first method, referred to as the indirect approach, the hig

In this article, we present a new method for visual face tracking that is carried out in a wavelet subspace. Initially, a wavelet representation for the face template is created, which spans a low-dimensional subspace of the image space. The video sequence frames, where the face is tracked, are then orthogonally projected into this subspace. This can be done efficiently through a small number of a

Recognition of human faces using a gallery of still or video images and a probe set of videos is systematically investigated using a probabilistic framework. In still-to-video recognition, where the gallery consists of still images, a time series state space model is proposed to fuse temporal information in a probe video, which simultaneously characterizes the kinematics and identity using a motio

A new exemplar-based probabilistic approach for face recognition in video sequences is presented. The approach has two stages: First, Exemplars, which are selected representatives from the raw video, are automatically extracted from gallery videos. The exemplars are used to summarize the gallery video information. In the second part, exemplars are then used as centers for probabilistic mixture dis

We present a technique for facial feature localization using a two-level hierarchical wavelet network. The first level wavelet network is used for face matching, and yields an affine transformation used for a rough approximation of feature locations. Second level wavelet networks for each feature are then used to fine-tune the feature locations. Construction of a training database containing hiera

A new exemplar-based probabilistic approach for face recognition in video sequences is presented. The approach has two stages: First, exemplars, which are selected representatives from the raw video, are automatically extracted from gallery videos. The exemplars are used to summarize the gallery video information. In the second part, exemplars are then used as centers for probabilistic mixture dis

Gait is a spatiooral phenomenon that typifies the motion characteristics of an individual. In this paper, we propose a view-based approach to recognize humans through gait. The width of the outer contour of the binarized silhouette of a walking person is chosen as the image feature. A set of stances or key frames that occur during the walk cycle of an individual is chosen. Euclidean distances of a

In this work we present an appearance-based 3-D Face Recognition approach that is able to recognize faces in video sequences, independent from face pose. For this we combine eigen light-fields with probabilistic propagation over time for evidence integration. Eigen light-fields allow us to build an appearance based 3-D model of an object; probabilistic methods for evidence integration are attracti

The aim of this work is to investigate how to exploit the temporal information in a video sequence for the task of face recognition. Following the approach in (Li and Chellappa, 2000), we propose a probabilistic model parameterized by a tracking state vector and a recognizing identity variable, simultaneously characterizing the kinematics and identity of humans. We then invoke a CONDENSATION (Isar

Wavelet networks (WNs) were introduced in 1992 as a combination of artificial neural radial basis function (RBF) networks and wavelet decomposition. Since then, however, WNs have received only a little attention. We believe that the potential of WNs has been generally underestimated. WNs have the advantage that the wavelet coefficients are directly related to the image data through the wavelet tra

In this paper, we first introduce the Gabor wavelet network (GWN) as a model-based approach for effective and efficient object representation. GWNs combine the advantages of the continuous wavelet transform with RBF networks. They have additional advantages such as invariance to some degree with respect to affine deformations. The use of Gabor filters enables the coding of geometrical and textural

In this article we present a new method for visual face tracking that is carried out in wavelet subspace. Firstly, a waveletre presentation for the face template is created, which spans a low dimensional subspace of the image space. The wavelet representation of the face is a point in this wavelet subspace. The video sequence frames in which the face is tracked are orthogonally projected into this

In this article we want to introduce first the Gabor wavelet network as a model based approach for an effective and efficient object representation. The Gabor wavelet network has several advantages such as invariance to some degree with respect to translation, rotation and dilation. Furthermore, the use of Gabor filters ensured that geometrical and textural object features are encoded. The feasibi

In this article we present a method for visual face tracking that is based on a wavelet representation of a face template. The wavelet representation allows arbitrary affine deformations of the facial image, it allows to generalize from an individual face template to a rather general face template and it allows to adapt the computational needs of the tracking algorithm to the computational resourc