Willem Sanberg

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Willem P Sanberg

Eindhoven University of Technology

Peter de With

V. Fremont

Université de Technologie de Compiègne

Vincent Fremont

Université de Technologie de Compiègne

Danilo Alves de Lima

UFLA - Federal University of Lavras

Radu Danescu

Technical University of Cluj-Napoca

Mahdi Rezaei

University of Leeds

Victor Zhang

Stanford University

J. Otamendi

Universidad Nacional de Rio Cuarto

Erhan Konukseven

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Papers by Willem Sanberg

Color-Based Free-Space Segmentation Using Online Disparity-Supervised Learning

by Peter de With and Willem Sanberg

2015 IEEE 18th International Conference on Intelligent Transportation Systems, 2015

This work contributes to vision processing for Advanced Driver Assist Systems (ADAS) and intellig... more This work contributes to vision processing for Advanced Driver Assist Systems (ADAS) and intelligent vehicle applications. We propose a color-only stixel segmentation framework to segment traffic scenes into free, drivable space and obstacles, which has a reduced latency to improve the real-time processing capabilities. Our system learns color appearance models for free-space and obstacle classes in an online and self-supervised fashion. To this end, it applies a disparitybased segmentation, which can run in the background of the critical system path, either with a time delay of several frames or at a frame rate that is only a third of that of the color-based algorithm. In parallel, the most recent video frame is analyzed solely with these learned color appearance models, without an actual disparity estimate and the corresponding latency. This translates into a reduced response time from data acquisition to data analysis, which is a critical property for high-speed ADAS. Our evaluation on two publicly available datasets, one of which we introduce as part of this work, shows that the color-only analysis can achieve similar or even better results in difficult imaging conditions, compared to the disparity-only method. Our system improves the quality of the free-space analysis, while simultaneously lowering the latency and the computational load.

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Adding Context Information to Video Analysis for Surveillance Applications

by Dennis W J M van de Wouw, Peter de With, and Willem Sanberg

Emerging Research on Networked Multimedia Communication Systems, 2016

Flexible Multi-modal Graph-Based Segmentation

by Peter de With and Willem Sanberg

Lecture Notes in Computer Science, 2013

ABSTRACT This paper aims at improving the well-known local variance segmentation method by adding... more ABSTRACT This paper aims at improving the well-known local variance segmentation method by adding extra signal modi and specific processing steps. As a key contribution, we extend the uni-modal segmentation method to perform multi-modal analysis, such that any number of signal modi available can be incorporated in a very flexible way. We have found that the use of a combined weight of luminance and depth values improves the segmentation score by 6.8%, for a large and challenging multi-modal dataset. Furthermore, we have developed an improved uni-modal texture-segmentation algorithm. This improvement relies on a clever choice of the color space and additional pre- and post-processing steps, by which we have increased the segmentation score on a challenging texture dataset by 2.1%. This gain is mainly preserved when using a different dataset with worse lighting conditions and different scene types.

Extending the Stixel World with online self-supervised color modeling for road-versus-obstacle segmentation

by Peter de With and Willem Sanberg

17th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2014

This work concentrates on vision processing for ADAS and intelligent vehicle applications. We pro... more This work concentrates on vision processing for ADAS and intelligent vehicle applications. We propose a color extension to the disparity-based Stixel World method, so that the road can be robustly distinguished from obstacles with respect to erroneous disparity measurements. Our extension learns color appearance models for road and obstacle classes in an online and self-supervised fashion. The algorithm is tightly integrated within the core of the optimization process of the original Stixel World, allowing for strong fusion of the disparity and color signals. We perform an extensive evaluation, including different self-supervised learning strategies and different color models. Our newly recorded, publicly available data set is intentionally focused on challenging traffic scenes with many low-texture regions, causing numerous disparity artifacts. In this evaluation, we increase the F-score of the drivable distance from 0.86 to 0.97, compared to a tuned version of the stateof-the-art baseline method. This clearly shows that our color extension increases the robustness of the Stixel World, by reducing the number of falsely detected obstacles while not deteriorating the detection of true obstacles.

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Color-Based Free-Space Segmentation Using Online Disparity-Supervised Learning

by Peter de With and Willem Sanberg

2015 IEEE 18th International Conference on Intelligent Transportation Systems, 2015

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Adding Context Information to Video Analysis for Surveillance Applications

by Dennis W J M van de Wouw, Peter de With, and Willem Sanberg

Emerging Research on Networked Multimedia Communication Systems, 2016

Flexible Multi-modal Graph-Based Segmentation

by Peter de With and Willem Sanberg

Lecture Notes in Computer Science, 2013