Our Keynote Talks

Rudolf Mester:
Predictive Visual Perception for Automotive Applications
Irish Machine Vision and Image Processing conference (IMVIP)
Galway, Ireland, August 2016
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Understanding the world around us while we are moving means to continuously maintain a dynamically changing representation of the environment, to make predictions about what to see next, and to correctly process those perceptions which were surprising, relative to our predictions. This principle is valid both for animate beings, as well as for technical systems that successfully participate in traffic. At the VSI Lab, we put special emphasis on this recursive / predictive approach to visual perception in ongoing projects for driver assistance and autonomous driving. These processing structures are complemented by statistical modeling of egomotion, environment, and the measurement process. In our opinion, this approach leads to particularly efficient systems, since computational ressources may be focussed on 'surprising' (thus rare) observations, and since this allows for a large reduction of search spaces in typical visual matching and tracking tasks. The talk will present examples for such predictive / recursive processing structures. Furthermore, recent results in the field of monocular, stereo, and multi-monocular (surround vision) applications will be shown.

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Rudolf Mester:
Predictive Video Processing for ADAS
Intelligent Vehicles (IV) Symposium - Workshop on holistic interfaces for environmental fusion models
Gothenburg, Sweden, June 2016
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Understanding the world around us while we are moving means continuously maintaining a dynamically changing representation of the environment, making predictions about what to see next, and correctly processing those perceptions which were surprising, relative to our predictions.
This principle is valid both for animate beings, as well as for technical systems that successfully participate in traffic. The VSI Lab at Frankfurt University puts special emphasis on this recursive / predictive approach to visual perception in ongoing projects for ADAS and autonomous driving. In our opinion, this approach leads to particularly efficient systems, since computational ressources may be focussed on 'surprising' (thus rare) observations, and since this allows for a large reduction of search spaces in typical visual matching and tracking tasks.
Furthermore, since the environment representation is actually closely coupled to the measuring process, and not a distant result at the end of a long processing pipeline, it allows for a simplified fusion of information from different sensors. This implies of course a more tight coupling between sensor data processing and interpretation. The talk will present examples for the such predictive / recursive processing structures and put the pros and cons up to discussion.

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Rudolf Mester:
Towards visual surround sensing: challenges, test data, and emerging methods.
International Conference on Computer Vision (ICCV) - Computer Vision for Road Scene Understanding and Autonomous Driving
Santiago, Chile, December 2015
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The talk presents work of the VSI group (Frankfurt) and the CVL group (Linköping) in the area of Visual (Surround) Sensing for cars, emphasizing methods for measuring visual motion reliably, and extracting 3D information from sets of trajectories over multiple frames. The algorithms presented here are characterized by a strong predictive / recursive character of the processing pipeline, and they involve stochastic models of vehicle dynamics, such as presented in the companion paper [Bradler et al., CVRSUAD 2015]. We show examples of diverse new test data sets, both in a multi-monocular surround view mode (AMUSE data set) as well as very realistic synthetic sequences (COnGRATS) that include precise pixelwise ground truth for 3D depth, optical flow, surface orientation, and semantic labeling. We conclude with an examples of how the diverse variants of the investigated environment perception methods (monocular, stereo, and multi-monocular) perform on real driving scene data.

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