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Author |
Jose Carlos Rubio; Joan Serrat; Antonio Lopez; Daniel Ponsa |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
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Title |
Multiple-target tracking for the intelligent headlights control |
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Conference Article |
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Year |
2010 |
Publication |
13th Annual International Conference on Intelligent Transportation Systems |
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903–910 |
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Intelligent Headlights |
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Abstract |
TA7.4
Intelligent vehicle lighting systems aim at automatically regulating the headlights' beam to illuminate as much of the road ahead as possible while avoiding dazzling other drivers. A key component of such a system is computer vision software that is able to distinguish blobs due to vehicles' headlights and rear lights from those due to road lamps and reflective elements such as poles and traffic signs. In a previous work, we have devised a set of specialized supervised classifiers to make such decisions based on blob features related to its intensity and shape. Despite the overall good performance, there remain challenging that have yet to be solved: notably, faint and tiny blobs corresponding to quite distant vehicles. In fact, for such distant blobs, classification decisions can be taken after observing them during a few frames. Hence, incorporating tracking could improve the overall lighting system performance by enforcing the temporal consistency of the classifier decision. Accordingly, this paper focuses on the problem of constructing blob tracks, which is actually one of multiple-target tracking (MTT), but under two special conditions: We have to deal with frequent occlusions, as well as blob splits and merges. We approach it in a novel way by formulating the problem as a maximum a posteriori inference on a Markov random field. The qualitative (in video form) and quantitative evaluation of our new MTT method shows good tracking results. In addition, we will also see that the classification performance of the problematic blobs improves due to the proposed MTT algorithm. |
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Address ![sorted by Address field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Madeira Island (Portugal) |
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ADAS @ adas @ RSL2010 |
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1422 |
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Author |
Ferran Diego; Daniel Ponsa; Joan Serrat; Antonio Lopez |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
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Title |
Vehicle geolocalization based on video synchronization |
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Conference Article |
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2010 |
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13th Annual International Conference on Intelligent Transportation Systems |
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1511–1516 |
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video alignment |
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Abstract |
TC8.6
This paper proposes a novel method for estimating the geospatial localization of a vehicle. I uses as input a georeferenced video sequence recorded by a forward-facing camera attached to the windscreen. The core of the proposed method is an on-line video synchronization which finds out the corresponding frame in the georeferenced video sequence to the one recorded at each time by the camera on a second drive through the same track. Once found the corresponding frame in the georeferenced video sequence, we transfer its geospatial information of this frame. The key advantages of this method are: 1) the increase of the update rate and the geospatial accuracy with regard to a standard low-cost GPS and 2) the ability to localize a vehicle even when a GPS is not available or is not reliable enough, like in certain urban areas. Experimental results for an urban environments are presented, showing an average of relative accuracy of 1.5 meters. |
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Address ![sorted by Address field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Madeira Island (Portugal) |
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2153-0009 |
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978-1-4244-7657-2 |
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ADAS @ adas @ DPS2010 |
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1423 |
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Ferran Diego; Jose Manuel Alvarez; Joan Serrat; Antonio Lopez |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
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Title |
Vision-based road detection via on-line video registration |
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Conference Article |
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Year |
2010 |
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13th Annual International Conference on Intelligent Transportation Systems |
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1135–1140 |
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video alignment; road detection |
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Abstract |
TB6.2
Road segmentation is an essential functionality for supporting advanced driver assistance systems (ADAS) such as road following and vehicle and pedestrian detection. Significant efforts have been made in order to solve this task using vision-based techniques. The major challenge is to deal with lighting variations and the presence of objects on the road surface. In this paper, we propose a new road detection method to infer the areas of the image depicting road surfaces without performing any image segmentation. The idea is to previously segment manually or semi-automatically the road region in a traffic-free reference video record on a first drive. And then to transfer these regions to the frames of a second video sequence acquired later in a second drive through the same road, in an on-line manner. This is possible because we are able to automatically align the two videos in time and space, that is, to synchronize them and warp each frame of the first video to its corresponding frame in the second one. The geometric transform can thus transfer the road region to the present frame on-line. In order to reduce the different lighting conditions which are present in outdoor scenarios, our approach incorporates a shadowless feature space which represents an image in an illuminant-invariant feature space. Furthermore, we propose a dynamic background subtraction algorithm which removes the regions containing vehicles in the observed frames which are within the transferred road region. |
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Address ![sorted by Address field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Madeira Island (Portugal) |
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2153-0009 |
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978-1-4244-7657-2 |
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ADAS |
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ADAS @ adas @ DAS2010 |
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1424 |
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Author |
Diego Alejandro Cheda; Daniel Ponsa; Antonio Lopez |
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Title |
Camera Egomotion Estimation in the ADAS Context |
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Conference Article |
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2010 |
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13th International IEEE Annual Conference on Intelligent Transportation Systems |
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1415–1420 |
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Camera-based Advanced Driver Assistance Systems (ADAS) have concentrated many research efforts in the last decades. Proposals based on monocular cameras require the knowledge of the camera pose with respect to the environment, in order to reach an efficient and robust performance. A common assumption in such systems is considering the road as planar, and the camera pose with respect to it as approximately known. However, in real situations, the camera pose varies along time due to the vehicle movement, the road slope, and irregularities on the road surface. Thus, the changes in the camera position and orientation (i.e., the egomotion) are critical information that must be estimated at every frame to avoid poor performances. This work focuses on egomotion estimation from a monocular camera under the ADAS context. We review and compare egomotion methods with simulated and real ADAS-like sequences. Basing on the results of our experiments, we show which of the considered nonlinear and linear algorithms have the best performance in this domain. |
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Address ![sorted by Address field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Madeira Island (Portugal) |
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2153-0009 |
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978-1-4244-7657-2 |
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ADAS |
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ADAS @ adas @ CPL2010 |
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1425 |
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Author |
Joan Serrat; J. Argemi; Juan J. Villanueva |
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Title |
Automatization of TW2 method using a knowledge-based image analysis system. |
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1991 |
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VIth International Congress of Auxology. |
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Address ![sorted by Address field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Madrid |
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ADAS @ adas @ SAV1991 |
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259 |
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Author |
Youssef El Rhabi; Simon Loic; Brun Luc; Josep Llados; Felipe Lumbreras |
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
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Title |
Information Theoretic Rotationwise Robust Binary Descriptor Learning |
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Conference Article |
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2016 |
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Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) |
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368-378 |
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In this paper, we propose a new data-driven approach for binary descriptor selection. In order to draw a clear analysis of common designs, we present a general information-theoretic selection paradigm. It encompasses several standard binary descriptor construction schemes, including a recent state-of-the-art one named BOLD. We pursue the same endeavor to increase the stability of the produced descriptors with respect to rotations. To achieve this goal, we have designed a novel offline selection criterion which is better adapted to the online matching procedure. The effectiveness of our approach is demonstrated on two standard datasets, where our descriptor is compared to BOLD and to several classical descriptors. In particular, it emerges that our approach can reproduce equivalent if not better performance as BOLD while relying on twice shorter descriptors. Such an improvement can be influential for real-time applications. |
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Address ![sorted by Address field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Mérida; Mexico; November 2016 |
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S+SSPR |
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DAG; ADAS; 600.097; 600.086 |
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Admin @ si @ RLL2016 |
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2871 |
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Author |
Jose Manuel Alvarez; Theo Gevers; Antonio Lopez |
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Title |
Learning Photometric Invariance from Diversified Color Model Ensembles |
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Conference Article |
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2009 |
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22nd IEEE Conference on Computer Vision and Pattern Recognition |
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565–572 |
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road detection |
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Color is a powerful visual cue for many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions affecting negatively the performance of the task at hand. Often, a reflection model (e.g., Lambertian or dichromatic reflectance) is used to derive color invariant models. However, those reflection models might be too restricted to model real-world scenes in which different reflectance mechanisms may hold simultaneously. Therefore, in this paper, we aim to derive color invariance by learning from color models to obtain diversified color invariant ensembles. First, a photometrical orthogonal and non-redundant color model set is taken on input composed of both color variants and invariants. Then, the proposed method combines and weights these color models to arrive at a diversified color ensemble yielding a proper balance between invariance (repeatability) and discriminative power (distinctiveness). To achieve this, the fusion method uses a multi-view approach to minimize the estimation error. In this way, the method is robust to data uncertainty and produces properly diversified color invariant ensembles. Experiments are conducted on three different image datasets to validate the method. From the theoretical and experimental results, it is concluded that the method is robust against severe variations in imaging conditions. The method is not restricted to a certain reflection model or parameter tuning. Further, the method outperforms state-of- the-art detection techniques in the field of object, skin and road recognition. |
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Address ![sorted by Address field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Miami (USA) |
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1063-6919 |
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978-1-4244-3992-8 |
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CVPR |
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ADAS;ISE |
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ADAS @ adas @ AGL2009 |
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1169 |
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Author |
Joan Serrat; Ferran Diego; Jose Manuel Alvarez; Felipe Lumbreras |
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Title |
Alignment of Videos Recorded from Moving Vehicles |
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Conference Article |
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2007 |
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in 14th International Conference on Image Analysis and Processing, |
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512–517 |
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Address ![sorted by Address field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Modena (Italia) |
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ADAS |
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ADAS @ adas @ SDA2007 |
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879 |
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Author |
Fadi Dornaika; Angel Sappa |
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Title |
Real-time Vehicle Ego-Motion using Stereo Pairs and Particle Filters |
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Conference Article |
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2007 |
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Int. Conf. on Image Analysis and Recognition, |
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4633 |
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469–480 |
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Address ![sorted by Address field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Montreal (Canada) |
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ADAS @ adas @ DoS2007a |
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813 |
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Author |
Jiaolong Xu; Peng Wang; Heng Yang; Antonio Lopez |
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Title |
Training a Binary Weight Object Detector by Knowledge Transfer for Autonomous Driving |
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2019 |
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IEEE International Conference on Robotics and Automation |
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2379-2384 |
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Autonomous driving has harsh requirements of small model size and energy efficiency, in order to enable the embedded system to achieve real-time on-board object detection. Recent deep convolutional neural network based object detectors have achieved state-of-the-art accuracy. However, such models are trained with numerous parameters and their high computational costs and large storage prohibit the deployment to memory and computation resource limited systems. Low-precision neural networks are popular techniques for reducing the computation requirements and memory footprint. Among them, binary weight neural network (BWN) is the extreme case which quantizes the float-point into just bit. BWNs are difficult to train and suffer from accuracy deprecation due to the extreme low-bit representation. To address this problem, we propose a knowledge transfer (KT) method to aid the training of BWN using a full-precision teacher network. We built DarkNet-and MobileNet-based binary weight YOLO-v2 detectors and conduct experiments on KITTI benchmark for car, pedestrian and cyclist detection. The experimental results show that the proposed method maintains high detection accuracy while reducing the model size of DarkNet-YOLO from 257 MB to 8.8 MB and MobileNet-YOLO from 193 MB to 7.9 MB. |
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Address ![sorted by Address field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Montreal; Canada; May 2019 |
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ICRA |
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ADAS; 600.124; 600.116; 600.118 |
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Admin @ si @ XWY2018 |
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3182 |
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