Siemens Aktiengesellschaft

Siemens Corporation

Siemens Corporate Research, Inc.

Siemens Vdo Automotive Ag

Continental Automotive Gmbh

Martin Pellkofer

Thorsten Köhler

Ying Zhu

Dorin Comaniciu

Claus Bahlmann

Visvanathan Ramesh

Gregory Baratoff

Ludwig Ertl

Binglong Xie

Jan Giebel

Andre Rieder

Thorsten KÖHLER

A speed limit assistant (SLA) system includes a camera based SLA, a map based SLA, and a fusion unit. The camera based SLA is configured to determine a first set of probabilities for an input image, wherein the probabilities indicate how likely the image includes a discrete set of speed limit signs. The map based SLA is configured to determine a second set of probabilities for an input coordinate, wherein the probabilities indicate how likely the coordinate is to correspond to one of a discrete set of speed limits. The fusion unit is configured to perform a Bayesian fusion on the first and second set of probabilities to determine a final speed limit from the discrete set of speed limits.

Multi-modal speed limit assistant

A method for training a system for detecting multi-class objects in an image or a video sequence is described. A common ensemble of weak classifiers for a set of object classes is identified. For each object class, a separate weighting scheme is adapted for the ensemble of weak classifiers. A method for detecting objects of multiple classes in an image or a video sequence is also disclosed. Each class is assigned a detector that is implemented by a weighted combination of weak classifiers such that all of the detectors are based on a common ensemble of weak classifiers. Then weights are individually set for each class.

Method for combining boosted classifiers for efficient multi-class object detection

A system and method for detecting and tracking an object is disclosed. A camera captures a video sequence comprised of a plurality of image frames. A processor receives the video sequence and analyzes each image frame to determine if an object is detected. The processor applies one or more classifiers to an object in each image frame and computes a confidence score based on the application of the one or more classifiers to the object. A database stores the one or more classifiers and vehicle training samples. A display displays the video sequence.

System and method for vehicle detection and tracking

An assistance system for motor vehicles has at least one monitoring unit monitoring a space external to a vehicle, in particular in a direction of travel of the motor vehicle, with a warning zone being defined in the monitored external space on each side of a lane. The warning zone is delimited by an earliest warning line and a latest warning line encompassing the respective lane marking or, as the case may be, boundary. An evaluation unit receives the data transmitted by the monitoring unit and generates a warning signal for an acoustic, visual and/or haptic warning and/or a steering intervention if the evaluation unit detects that a defined warning threshold lying within the warning zone is exceeded. The warning signal is issued only as a function of further planned driver-adaptive conditions.

Assistance system for motor vehicles

The present invention is directed to a system and method for on-road vehicle detection. A video sequence is received that is comprised of a plurality of image frames. A potential vehicle appearance is identified in an image frame. Known vehicle appearance information and scene geometry information are used to formulate initial hypotheses about vehicle appearance. The potential vehicle appearance is tracked over multiple successive image frames. Potential motion trajectories for the potential vehicle appearance are identified over the multiple image frames. Knowledge fusion of appearance, scene geometry and motion information models are applied to each image frame containing the trajectories. A confidence score is calculated for each trajectory. A trajectory with a high confidence score is determined to represent a vehicle appearance.

System and method for on-road detection of a vehicle using knowledge fusion

A method of detecting a feature of a vehicle in an image of a vehicle includes providing a digitized image of a vehicle, providing a first filter mask over a first subdomain of the image, where the filter mask is placed to detect a feature in the image, calculating a function of a gradient of the image inside the first masked subdomain, and detecting the presence or absence of a vehicle feature within the first masked subdomain based on the value of the gradient function.

System and method for detecting features from images of vehicles

A method for detecting and recognizing at least one traffic sign is disclosed. A video sequence having a plurality of image frames is received. One or more filters are used to measure features in at least one image frame indicative of an object of interest. The measured features are combined and aggregated into a score indicating possible presence of an object. The scores are fused over multiple image frames for a robust detection. If a score indicates possible presence of an object in an area of the image frame, the area is aligned with a model. A determination is then made as to whether the area indicates a traffic sign. If the area indicates a traffic sign, the area is classified into a particular type of traffic sign. The present invention is also directed to training a system to detect and recognize traffic signs.

Method for traffic sign detection

A system and method for detecting a passing vehicle is disclosed. A video sequence comprising a plurality of image frames is received. Image intensity is measured and image motion is estimated in each image frame. A hypothesis model describing background dynamics is formulated. The measured image intensity and motion estimation is used to determine if the background dynamics has been violated in a given image frame. If the background dynamics has been violated, motion coherency is used to determine whether the violation of the background dynamics is caused by a passing vehicle.

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Pentling, Germany

Martin Pellkofer