In this paper, we present a vehicle safety application based on data gathered by a laser scanner and two short-range radars that recognize unavoidable collisions with stationary objects before they take place to trigger restraint systems. Two different software modules that perform the processing of raw data and deliver a description of the vehicle's environment are compared. A comprehensive experimental evaluation based on relevant crash and noncrash scenarios is presented.

Computers have been widely deployed to our daily lives, but human-computer interaction still lacks intuition. Researchers intend to resolve these shortcomings by augmenting traditional systems with human-like interaction capabilities. Knowledge about human emotion, behavior, and intention is necessary to construct convenient interaction mechanisms. Today, dedicated hardware often infers the emotional state from human body measures. Similar to humans interpreting facial expressions, our approach acquires video information using standard hardware that does not interfere with people to accomplish this task. It exploits model-based techniques that accurately localize facial features, seamlessly track them through image sequences, and finally interpret the visible information. We make use of state-of-the-art techniques and specifically adapt most of the components involved to this scenario, which provides high accuracy and real-time capability. We base our experimental evaluation on publicly available databases and compare its results to related approaches. Our proof-of-concept demonstrates the feasibility of our approach and shows promising for integration into various applications.

Model-based techniques have proven to be successful in interpreting the large amount of information contained in images. Associated fitting algorithms search for the global optimum of an objective function, which should correspond to the best model fit in a given image. Although fitting algorithms have been the subject of intensive research and evaluation, the objective function is usually designed ad hoc, based on implicit and domain-dependent knowledge. In this article, we address the root of the problem by learning more robust objective functions. First, we formulate a set of desirable properties for objective functions and give a concrete example function that has these properties. Then, we propose a novel approach that learns an objective function from training data generated by manual image annotations and this ideal objective function. In this approach, critical decisions such as feature selection are automated, and the remaining manual steps hardly require domain-dependent knowledge. Furthermore, an extensive empirical evaluation demonstrates that the obtained objective functions yield more robustness. Learned objective functions enable fitting algorithms to determine the best model fit more accurately than with designed objective functions.