133 research outputs found

    Supervised learning and inference of semantic information from road scene images

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    Premio Extraordinario de Doctorado de la UAH en el año académico 2013-2014Nowadays, vision sensors are employed in automotive industry to integrate advanced functionalities that assist humans while driving. However, autonomous vehicles is a hot field of research both in academic and industrial sectors and entails a step beyond ADAS. Particularly, several challenges arise from autonomous navigation in urban scenarios due to their naturalistic complexity in terms of structure and dynamic participants (e.g. pedestrians, vehicles, vegetation, etc.). Hence, providing image understanding capabilities to autonomous robotics platforms is an essential target because cameras can capture the 3D scene as perceived by a human. In fact, given this need for 3D scene understanding, there is an increasing interest on joint objects and scene labeling in the form of geometry and semantic inference of the relevant entities contained in urban environments. In this regard, this Thesis tackles two challenges: 1) the prediction of road intersections geometry and, 2) the detection and orientation estimation of cars, pedestrians and cyclists. Different features extracted from stereo images of the KITTI public urban dataset are employed. This Thesis proposes a supervised learning of discriminative models that rely on strong machine learning techniques for data mining visual features. For the first task, we use 2D occupancy grid maps that are built from the stereo sequences captured by a moving vehicle in a mid-sized city. Based on these bird?s eye view images, we propose a smart parameterization of the layout of straight roads and 4 intersecting roads. The dependencies between the proposed discrete random variables that define the layouts are represented with Probabilistic Graphical Models. Then, the problem is formulated as a structured prediction, in which we employ Conditional Random Fields (CRF) for learning and convex Belief Propagation (dcBP) and Branch and Bound (BB) for inference. For the validation of the proposed methodology, a set of tests are carried out, which are based on real images and synthetic images with varying levels of random noise. In relation to the object detection and orientation estimation challenge in road scenes, this Thesis goal is to compete in the international challenge known as KITTI evaluation benchmark, which encourages researchers to push forward the current state of the art on visual recognition methods, particularized for 3D urban scene understanding. This Thesis proposes to modify the successful part-based object detector known as DPM in order to learn richer models from 2.5D data (color and disparity). Therefore, we revisit the DPM framework, which is based on HOG features and mixture models trained with a latent SVM formulation. Next, this Thesis performs a set of modifications on top of DPM: I) An extension to the DPM training pipeline that accounts for 3D-aware features. II) A detailed analysis of the supervised parameter learning. III) Two additional approaches: "feature whitening" and "stereo consistency check". Additionally, a) we analyze the KITTI dataset and several subtleties regarding to the evaluation protocol; b) a large set of cross-validated experiments show the performance of our contributions and, c) finally, our best performing approach is publicly ranked on the KITTI website, being the first one that reports results with stereo data, yielding an increased object detection precision (3%-6%) for the class 'car' and ranking first for the class cyclist

    Supervised learning and inference of semantic information from road scene images

    Get PDF
    Premio Extraordinario de Doctorado de la UAH en el año académico 2013-2014Nowadays, vision sensors are employed in automotive industry to integrate advanced functionalities that assist humans while driving. However, autonomous vehicles is a hot field of research both in academic and industrial sectors and entails a step beyond ADAS. Particularly, several challenges arise from autonomous navigation in urban scenarios due to their naturalistic complexity in terms of structure and dynamic participants (e.g. pedestrians, vehicles, vegetation, etc.). Hence, providing image understanding capabilities to autonomous robotics platforms is an essential target because cameras can capture the 3D scene as perceived by a human. In fact, given this need for 3D scene understanding, there is an increasing interest on joint objects and scene labeling in the form of geometry and semantic inference of the relevant entities contained in urban environments. In this regard, this Thesis tackles two challenges: 1) the prediction of road intersections geometry and, 2) the detection and orientation estimation of cars, pedestrians and cyclists. Different features extracted from stereo images of the KITTI public urban dataset are employed. This Thesis proposes a supervised learning of discriminative models that rely on strong machine learning techniques for data mining visual features. For the first task, we use 2D occupancy grid maps that are built from the stereo sequences captured by a moving vehicle in a mid-sized city. Based on these bird?s eye view images, we propose a smart parameterization of the layout of straight roads and 4 intersecting roads. The dependencies between the proposed discrete random variables that define the layouts are represented with Probabilistic Graphical Models. Then, the problem is formulated as a structured prediction, in which we employ Conditional Random Fields (CRF) for learning and convex Belief Propagation (dcBP) and Branch and Bound (BB) for inference. For the validation of the proposed methodology, a set of tests are carried out, which are based on real images and synthetic images with varying levels of random noise. In relation to the object detection and orientation estimation challenge in road scenes, this Thesis goal is to compete in the international challenge known as KITTI evaluation benchmark, which encourages researchers to push forward the current state of the art on visual recognition methods, particularized for 3D urban scene understanding. This Thesis proposes to modify the successful part-based object detector known as DPM in order to learn richer models from 2.5D data (color and disparity). Therefore, we revisit the DPM framework, which is based on HOG features and mixture models trained with a latent SVM formulation. Next, this Thesis performs a set of modifications on top of DPM: I) An extension to the DPM training pipeline that accounts for 3D-aware features. II) A detailed analysis of the supervised parameter learning. III) Two additional approaches: "feature whitening" and "stereo consistency check". Additionally, a) we analyze the KITTI dataset and several subtleties regarding to the evaluation protocol; b) a large set of cross-validated experiments show the performance of our contributions and, c) finally, our best performing approach is publicly ranked on the KITTI website, being the first one that reports results with stereo data, yielding an increased object detection precision (3%-6%) for the class 'car' and ranking first for the class cyclist

    Learning to Propagate Labels on Graphs: An Iterative Multitask Regression Framework for Semi-supervised Hyperspectral Dimensionality Reduction

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    Hyperspectral dimensionality reduction (HDR), an important preprocessing step prior to high-level data analysis, has been garnering growing attention in the remote sensing community. Although a variety of methods, both unsupervised and supervised models, have been proposed for this task, yet the discriminative ability in feature representation still remains limited due to the lack of a powerful tool that effectively exploits the labeled and unlabeled data in the HDR process. A semi-supervised HDR approach, called iterative multitask regression (IMR), is proposed in this paper to address this need. IMR aims at learning a low-dimensional subspace by jointly considering the labeled and unlabeled data, and also bridging the learned subspace with two regression tasks: labels and pseudo-labels initialized by a given classifier. More significantly, IMR dynamically propagates the labels on a learnable graph and progressively refines pseudo-labels, yielding a well-conditioned feedback system. Experiments conducted on three widely-used hyperspectral image datasets demonstrate that the dimension-reduced features learned by the proposed IMR framework with respect to classification or recognition accuracy are superior to those of related state-of-the-art HDR approaches

    Multi-View Face Recognition From Single RGBD Models of the Faces

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    This work takes important steps towards solving the following problem of current interest: Assuming that each individual in a population can be modeled by a single frontal RGBD face image, is it possible to carry out face recognition for such a population using multiple 2D images captured from arbitrary viewpoints? Although the general problem as stated above is extremely challenging, it encompasses subproblems that can be addressed today. The subproblems addressed in this work relate to: (1) Generating a large set of viewpoint dependent face images from a single RGBD frontal image for each individual; (2) using hierarchical approaches based on view-partitioned subspaces to represent the training data; and (3) based on these hierarchical approaches, using a weighted voting algorithm to integrate the evidence collected from multiple images of the same face as recorded from different viewpoints. We evaluate our methods on three datasets: a dataset of 10 people that we created and two publicly available datasets which include a total of 48 people. In addition to providing important insights into the nature of this problem, our results show that we are able to successfully recognize faces with accuracies of 95% or higher, outperforming existing state-of-the-art face recognition approaches based on deep convolutional neural networks

    Player detection in field sports

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    We describe a method for player detection in field sports with a fixed camera set-up based on a new player feature extraction strategy. The proposed method detects players in static images with a sliding window technique. First, we compute a binary edge image and then the detector window is shifted over the edge regions. Given a set of binary edges in a sliding window, we introduce and solve a particular diffusion equation to generate a shape information image. The proposed diffusion to generate a shape information image is the key stage and the main theoretical contribution in our new algorithm. It removes the appearance variations of an object while preserving the shape information. It also enables the use of polar and Fourier transforms in the next stage to achieve scale and rotation invariant feature extraction. A Support Vector Machine (SVM) classifier is used to assign either player or non-player class inside a detector window. We evaluate our approach on three different field hockey datasets. In general, results show that the proposed feature extraction is effective, and performs competitive results compared to the state-of-the-art methods
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