Papers by Willem P Sanberg
IS&T International Symposium on Electronic Imaging Science and Technology, Jan 29, 2017
DOI to the publisher's website. • The final author version and the galley proof are versions of t... more DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:
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.
Radar-based Object Classification in ADAS with Hardware-Aware NAS and Input Region Scaling
DOI to the publisher's website. • The final author version and the galley proof are versions of t... more DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:
Scene alignment for images recorded from different viewpoints is a challenging task, especially c... more Scene alignment for images recorded from different viewpoints is a challenging task, especially considering strong parallax effects. This work proposes a diorama-box model for a 2.5D hierarchical alignment approach, which is specifically designed for image registration from a moving vehicle using a stereo camera. For this purpose, the Stixel World algorithm is used to partition the scene into super-pixels, which are transformed to 3D. This model is further refined by assigning a slanting orientation to each stixel and by interpolating between stixels, to prevent gaps in the 3D model. The resulting alignment shows promising results, where under normal viewing conditions, more than 96% of all annotated points are registered with an alignment error up to 5 pixels at a resolution of 1920 × 1440 pixels, executing at near-real time performance (4 fps) for the intended application.
Quantization-Aware Neural Architecture Search with Hyperparameter Optimization for Industrial Predictive Maintenance Applications
Computer vision for advanced driver assistance systems
ASTEROIDS: A Stixel Tracking Extrapolation-Based Relevant Obstacle Impact Detection System
IEEE transactions on intelligent vehicles, Mar 1, 2021
This paper presents a vision-based collision-warning system for ADAS in intelligent vehicles, wit... more This paper presents a vision-based collision-warning system for ADAS in intelligent vehicles, with a focus on urban scenarios. In most current systems, collision warnings are based on radar, or on monocular vision using pattern recognition. Since detecting collisions is a core functionality of intelligent vehicles, redundancy is essential, so that we explore the use of stereo vision. First, our approach is generic and class-agnostic, since it can detect general obstacles that are on a colliding path with the ego-vehicle without relying on semantic information. The framework estimates disparity and flow from a stereo video stream and calculates stixels. Then, the second contribution is the use of the new asteroids concept as a consecutive step. This step samples particles based on a probabilistic uncertainty analysis of the measurement process to model potential collisions. Third, this is all enclosed in a Bayesian histogram filter around a newly introduced time-to-collision versus angle-of-impact state space. The evaluation shows that the system correctly avoids any false warnings on the real-world KITTI dataset, detects all collisions in a newly simulated dataset when the obstacle is higher than 0.4 m, and performs excellent on our new qualitative real-world data with near-collisions, both in daytime and nighttime conditions.
IS&T International Symposium on Electronic Imaging Science and Technology, Jan 13, 2019
This paper explores the use of stixels in a probabilistic stereo vision-based collision-warning s... more This paper explores the use of stixels in a probabilistic stereo vision-based collision-warning system that can be part of an ADAS for intelligent vehicles. In most current systems, collision warnings are based on radar or on monocular vision using pattern recognition (and ultra-sound for park assist). Since detecting collisions is such a core functionality of intelligent vehicles, redundancy is key. Therefore, we explore the use of stereo vision for reliable collision prediction. Our algorithm consists of a Bayesian histogram filter that provides the probability of collision for multiple interception regions and angles towards the vehicle. This could additionally be fused with other sources of information in larger systems. Our algorithm builds upon the disparity Stixel World that has been developed for efficient automotive vision applications. Combined with image flow and uncertainty modeling, our system samples and propagates asteroids, which are dynamic particles that can be utilized for collision prediction. At best, our independent system detects all 31 simulated collisions (2 false warnings), while this setting generates 12 false warnings on the real-world data.
DOI to the publisher's website. • The final author version and the galley proof are versions of t... more DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:
DOI to the publisher's website. • The final author version and the galley proof are versions of t... more DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:
Lecture Notes in Computer Science, 2013
This paper aims at improving the well-known local variance segmentation method by adding extra si... more 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.
Efficient-DASH: Automated Radar Neural Network Design Across Tasks and Datasets
2023 IEEE Intelligent Vehicles Symposium (IV)
Block-Level Surrogate Models for Inference Time Estimation in Hardware-Aware Neural Architecture Search
Lecture Notes in Computer Science, 2023
arXiv (Cornell University), Apr 8, 2016
Recently, vision-based Advanced Driver Assist Systems have gained broad interest. In this work, w... more Recently, vision-based Advanced Driver Assist Systems have gained broad interest. In this work, we investigate free-space detection, for which we propose to employ a Fully Convolutional Network (FCN). We show that this FCN can be trained in a selfsupervised manner and achieve similar results compared to training on manually annotated data, thereby reducing the need for large manually annotated training sets. To this end, our selfsupervised training relies on a stereo-vision disparity system, to automatically generate (weak) training labels for the color-based FCN. Additionally, our self-supervised training facilitates online training of the FCN instead of offline. Consequently, given that the applied FCN is relatively small, the free-space analysis becomes highly adaptive to any traffic scene that the vehicle encounters. We have validated our algorithm using publicly available data and on a new challenging benchmark dataset that is released with this paper. Experiments show that the online training boosts performance with 5% when compared to offline training, both for F max and AP.
Radar-based Object Classification in ADAS with Hardware-Aware NAS and Input Region Scaling
2023 IEEE Radar Conference (RadarConf23)
Computer vision for advanced driver assistance systems
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.
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.
Scene alignment for images recorded from different viewpoints is a challenging task, especially c... more Scene alignment for images recorded from different viewpoints is a challenging task, especially considering strong parallax effects. This work proposes a diorama-box model for a 2.5D hierarchical alignment approach, which is specifically designed for image registration from a moving vehicle using a stereo camera. For this purpose, the Stixel World algorithm is used to partition the scene into super-pixels, which are transformed to 3D. This model is further refined by assigning a slanting orientation to each stixel and by interpolating between stixels, to prevent gaps in the 3D model. The resulting alignment shows promising results, where under normal viewing conditions, more than 96% of all annotated points are registered with an alignment error up to 5 pixels at a resolution of 1920x1440 pixels, executing at near-real time performance (4 fps) for the intended application.
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Papers by Willem P Sanberg