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Carme Julia, Angel Sappa, Felipe Lumbreras, Joan Serrat and Antonio Lopez. 2008. Rank Estimation in 3D Multibody Motion Segmentation. Electronic Letters, 44(4), 279–280.
Abstract: A novel technique for rank estimation in 3D multibody motion segmentation is proposed. It is based on the study of the frequency spectra of moving rigid objects and does not use or assume a prior knowledge of the objects contained in the scene (i.e. number of objects and motion). The significance of rank estimation on multibody motion segmentation results is shown by using two motion segmentation algorithms over both synthetic and real data.
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Joan Serrat, Ferran Diego, Felipe Lumbreras, Jose Manuel Alvarez, Antonio Lopez and C. Elvira. 2008. Dynamic Comparison of Headlights. Journal of Automobile Engineering, 222(5), 643–656.
Keywords: video alignment
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Yu Jie, Jaume Amores, N. Sebe, Petia Radeva and Tian Qi. 2008. Distance Learning for Similarity Estimation.
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Angel Sappa, Fadi Dornaika, Daniel Ponsa, David Geronimo and Antonio Lopez. 2008. An Efficient Approach to Onboard Stereo Vision System Pose Estimation. TITS, 9(3), 476–490.
Abstract: This paper presents an efficient technique for estimating the pose of an onboard stereo vision system relative to the environment’s dominant surface area, which is supposed to be the road surface. Unlike previous approaches, it can be used either for urban or highway scenarios since it is not based on a specific visual traffic feature extraction but on 3-D raw data points. The whole process is performed in the Euclidean space and consists of two stages. Initially, a compact 2-D representation of the original 3-D data points is computed. Then, a RANdom SAmple Consensus (RANSAC) based least-squares approach is used to fit a plane to the road. Fast RANSAC fitting is obtained by selecting points according to a probability function that takes into account the density of points at a given depth. Finally, stereo camera height and pitch angle are computed related to the fitted road plane. The proposed technique is intended to be used in driverassistance systems for applications such as vehicle or pedestrian detection. Experimental results on urban environments, which are the most challenging scenarios (i.e., flat/uphill/downhill driving, speed bumps, and car’s accelerations), are presented. These results are validated with manually annotated ground truth. Additionally, comparisons with previous works are presented to show the improvements in the central processing unit processing time, as well as in the accuracy of the obtained results.
Keywords: Camera extrinsic parameter estimation, ground plane estimation, onboard stereo vision system
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Fadi Dornaika and Angel Sappa. 2008. Evaluation of an Appearance-based 3D Face Tracker using Dense 3D Data.
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Hugo Berti, Angel Sappa and Osvaldo Agamennoni. 2008. Improved Dynamic Window Approach by Using Lyapunov Stability Criteria.
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Fadi Dornaika and Angel Sappa. 2009. A Featureless and Stochastic Approach to On-board Stereo Vision System Pose. IMAVIS, 27(9), 1382–1393.
Abstract: This paper presents a direct and stochastic technique for real-time estimation of on-board stereo head’s position and orientation. Unlike existing works which rely on feature extraction either in the image domain or in 3D space, our proposed approach directly estimates the unknown parameters from the stream of stereo pairs’ brightness. The pose parameters are tracked using the particle filtering framework which implicitly enforces the smoothness constraints on the estimated parameters. The proposed technique can be used with a driver assistance applications as well as with augmented reality applications. Extended experiments on urban environments with different road geometries are presented. Comparisons with a 3D data-based approach are presented. Moreover, we provide a performance study aiming at evaluating the accuracy of the proposed approach.
Keywords: On-board stereo vision system; Pose estimation; Featureless approach; Particle filtering; Image warping
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Carme Julia, Angel Sappa, Felipe Lumbreras, Joan Serrat and Antonio Lopez. 2009. An iterative multiresolution scheme for SFM with missing data. JMIV, 34(3), 240–258.
Abstract: Several techniques have been proposed for tackling the Structure from Motion problem through factorization in the case of missing data. However, when the percentage of unknown data is high, most of them may not perform as well as expected. Focussing on this problem, an iterative multiresolution scheme, which aims at recovering missing entries in the originally given input matrix, is proposed. Information recovered following a coarse-to-fine strategy is used for filling in the missing entries. The objective is to recover, as much as possible, missing data in the given matrix.
Thus, when a factorization technique is applied to the partially or totally filled in matrix, instead of to the originally given input one, better results will be obtained. An evaluation study about the robustness to missing and noisy data is reported.
Experimental results obtained with synthetic and real video sequences are presented to show the viability of the proposed approach.
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Daniel Ponsa and Antonio Lopez. 2009. Variance reduction techniques in particle-based visual contour Tracking. PR, 42(11), 2372–2391.
Abstract: This paper presents a comparative study of three different strategies to improve the performance of particle filters, in the context of visual contour tracking: the unscented particle filter, the Rao-Blackwellized particle filter, and the partitioned sampling technique. The tracking problem analyzed is the joint estimation of the global and local transformation of the outline of a given target, represented following the active shape model approach. The main contributions of the paper are the novel adaptations of the considered techniques on this generic problem, and the quantitative assessment of their performance in extensive experimental work done.
Keywords: Contour tracking; Active shape models; Kalman filter; Particle filter; Importance sampling; Unscented particle filter; Rao-Blackwellization; Partitioned sampling
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Daniel Ponsa, Joan Serrat and Antonio Lopez. 2011. On-board image-based vehicle detection and tracking. TIM, 33(7), 783–805.
Abstract: In this paper we present a computer vision system for daytime vehicle detection and localization, an essential step in the development of several types of advanced driver assistance systems. It has a reduced processing time and high accuracy thanks to the combination of vehicle detection with lane-markings estimation and temporal tracking of both vehicles and lane markings. Concerning vehicle detection, our main contribution is a frame scanning process that inspects images according to the geometry of image formation, and with an Adaboost-based detector that is robust to the variability in the different vehicle types (car, van, truck) and lighting conditions. In addition, we propose a new method to estimate the most likely three-dimensional locations of vehicles on the road ahead. With regards to the lane-markings estimation component, we have two main contributions. First, we employ a different image feature to the other commonly used edges: we use ridges, which are better suited to this problem. Second, we adapt RANSAC, a generic robust estimation method, to fit a parametric model of a pair of lane markings to the image features. We qualitatively assess our vehicle detection system in sequences captured on several road types and under very different lighting conditions. The processed videos are available on a web page associated with this paper. A quantitative evaluation of the system has shown quite accurate results (a low number of false positives and negatives) at a reasonable computation time.
Keywords: vehicle detection
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