In the context of driver assistance, an accurate and reliable prediction of the vehicle's trajectory is beneficial. This can be useful either to increase the flexibility of comfort systems or, in the more interesting case, to detect potentially dangerous situations as early as possible. In this contribution, a novel approach for trajectory prediction is proposed which has the capability to predict the vehicle's trajectory several seconds in advance, the so called long-term prediction. To achieve this, previously observed motion patterns are used to infer a joint probability distribution as motion model. Using this distribution, a trajectory can be predicted by calculating the probability for the future motion, conditioned on the current observed history motion pattern.The advantage of the probabilistic modeling is that the result is not only a prediction, but rather a whole distribution over the future trajectories and a specific prediction can be made by the evaluation of the statistical properties, e.g. the mean of this conditioned distribution. Additionally, an evaluation of the variance can be used to examine the reliability of the prediction.

Building on the current understanding of neural architecture of the visual cortex, we present a graphical model for learning and classification of motion patterns in videos. The model is composed of an arbitrary amount of Hidden Markov Models (HMMs) with shared Gaussian mixture models. The novel extension of our model is the use of additional Markov chain, serving as a switch for indicating the currently active HMM. We therefore call the model a Switching Hidden Markov Model (SHMM). SHMM learns from input optical flow in an unsupervised fashion. Functionality of the model is tested with artificially simulated time sequences. Tests with real videos show that the model is capable of learning and recognition of motion activities of single individuals, and for classification of motion patterns exhibited by groups of people. Classification rates of about 75 percent for real videos are satisfactory taking into account a relative simplicity of the model.The research leading to these results has received funding from the European Community's Seventh Framework Programme under grant agreement n 215866, project SEARISE.

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Automatic head pose estimation plays an important part in the development of human machine interfaces. This paper proposes a fast and frugal method for accurate and person-independent head pose estimation based on range images. Head pose estimation is treated as a non-linear regression problem and addressed with Synchronized Submanifold Embedding (SSE). The offline training step exploits the local linear structure of label and feature space for a cross-wise synchronization of pose samples from different subjects. Based on this, Multi-class Linear Discriminant Analysis (M-LDA) identifies a dimensionality-reducing linear projection, which diminishes non head pose related information. New samples are then projected into this lower dimensional feature space and classified based on training samples within their local neighborhood. In case of sequential data, the occurrence of outliers can be reduced using a reasonable preselection of neighborhood candidates based on tracking of pose changes. The experimental results on a publicly available database prove that the proposed algorithm can handle a large range of pose changes and outperforms existing methods in accuracy.

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