Technische Universitat Darmstadt

Toyota Motor Europe Nv/sa

Adc Automotive Distance Control Systems Gmbh

Bernt Schiele

Gabriel Othmezouri

Ichiro Sakata

Stefan Lüke

Konrad Schindler

Stefan Roth

Stefan Walk

Nikodem Majer

Christian Wojek

An image processor () has a window selector for choosing a detection window within the image, and a self similarity computation part () for determining self-similarity information for a group of the pixels in any part of the detection window, to represent an amount of self-similarity of that group to other groups in any other part of the detector window, and for repeating the determination for groups in all parts of the detection window, to generate a global self similarity descriptor for the detection window. A classifier () is used for classifying whether an object is present based on the global self-similarity descriptor. By using global self-similarity rather than local similarities more information is captured which can lead to better classification. In particular, it helps enable recognition of more distant self-similarities inherent in the object, and self-similarities present at any scale.

Detection of objects in an image using self similarities

Methods and apparatus are described for monocular 3D human pose estimation and tracking, which are able to recover poses of people in realistic street conditions captured using a monocular, potentially moving camera. Embodiments of the present invention provide a three-stage process involving estimating () a 3D pose of each of the multiple objects using an output of 2D tracking-by detection () and 2D viewpoint estimation (). The present invention provides a sound Bayesian formulation to address the above problems. The present invention can provide articulated 3D tracking in realistic street conditions. The present invention provides methods and apparatus for people detection and 2D pose estimation combined with a dynamic motion prior. The present invention provides not only 2D pose estimation for people in side views, it goes beyond this by estimating poses in 3D from multiple viewpoints. The estimation of poses is done in monocular images, and does not require stereo images. Also the present invention does not require detection of characteristic poses of people.

Monocular 3D pose estimation and tracking by detection

The invention relates to a method and an object detection device for analyzing objects in the environment and/or scenes in the environment. The object detection device includes a data processing and/or evaluation device. In the data processing and/or evaluation device, image data (x) is evaluated on the basis of a Conditional Random Field (CRF) model and the CRF model provides additional object nodes (o) which take into account information from an object detector.

Method and device for analyzing surrounding objects and/or surrounding scenes, such as for object and scene class segmenting

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Darmstadt, Germany

Bernt Schiele