Pedestrian detection in uncontrolled environments using stereo and biometric information

A method for pedestrian detection from challenging real world outdoor scenes is presented in this paper. This technique is able to extract multiple pedestrians, of varying orientations and appearances, from a scene even when faced with large and multiple occlusions. The technique is also robust to changing background lighting conditions and effects, such as shadows. The technique applies an enhanced method from which reliable disparity information can be obtained even from untextured homogeneous areas within a scene. This is used in conjunction with ground plane estimation and biometric information,to obtain reliable pedestrian regions. These regions are robust to erroneous areas of disparity data and also to severe pedestrian occlusion, which often occurs in unconstrained scenarios

Optimal Camera Placement to measure Distances Conservativly Regarding Static and Dynamic Obstacles

In modern production facilities industrial robots and humans are supposed to interact sharing a common working area. In order to avoid collisions, the distances between objects need to be measured conservatively which can be done by a camera network. To estimate the acquired distance, unmodelled objects, e.g., an interacting human, need to be modelled and distinguished from premodelled objects like workbenches or robots by image processing such as the background subtraction method. The quality of such an approach massively depends on the settings of the camera network, that is the positions and orientations of the individual cameras. Of particular interest in this context is the minimization of the error of the distance using the objects modelled by the background subtraction method instead of the real objects. Here, we show how this minimization can be formulated as an abstract optimization problem. Moreover, we state various aspects on the implementation as well as reasons for the selection of a suitable optimization method, analyze the complexity of the proposed method and present a basic version used for extensive experiments.Comment: 9 pages, 10 figure

Clustering files of chemical structures using the Szekely-Rizzo generalization of Ward's method

Ward's method is extensively used for clustering chemical structures represented by 2D fingerprints. This paper compares Ward clusterings of 14 datasets (containing between 278 and 4332 molecules) with those obtained using the Szekely–Rizzo clustering method, a generalization of Ward's method. The clusters resulting from these two methods were evaluated by the extent to which the various classifications were able to group active molecules together, using a novel criterion of clustering effectiveness. Analysis of a total of 1400 classifications (Ward and Székely–Rizzo clustering methods, 14 different datasets, 5 different fingerprints and 10 different distance coefficients) demonstrated the general superiority of the Székely–Rizzo method. The distance coefficient first described by Soergel performed extremely well in these experiments, and this was also the case when it was used in simulated virtual screening experiments