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Alejandro Gonzalez Alzate, Gabriel Villalonga, Jiaolong Xu, David Vazquez, Jaume Amores, & Antonio Lopez. (2015). Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. In IEEE Intelligent Vehicles Symposium IV2015 (pp. 356–361).
Abstract: Despite recent significant advances, pedestrian detection continues to be an extremely challenging problem in real scenarios. In order to develop a detector that successfully operates under these conditions, it becomes critical to leverage upon multiple cues, multiple imaging modalities and a strong multi-view classifier that accounts for different pedestrian views and poses. In this paper we provide an extensive evaluation that gives insight into how each of these aspects (multi-cue, multimodality and strong multi-view classifier) affect performance both individually and when integrated together. In the multimodality component we explore the fusion of RGB and depth maps obtained by high-definition LIDAR, a type of modality that is only recently starting to receive attention. As our analysis reveals, although all the aforementioned aspects significantly help in improving the performance, the fusion of visible spectrum and depth information allows to boost the accuracy by a much larger margin. The resulting detector not only ranks among the top best performers in the challenging KITTI benchmark, but it is built upon very simple blocks that are easy to implement and computationally efficient. These simple blocks can be easily replaced with more sophisticated ones recently proposed, such as the use of convolutional neural networks for feature representation, to further improve the accuracy.
Keywords: Pedestrian Detection
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Daniel Hernandez, Alejandro Chacon, Antonio Espinosa, David Vazquez, Juan Carlos Moure, & Antonio Lopez. (2016). Embedded real-time stereo estimation via Semi-Global Matching on the GPU. In 16th International Conference on Computational Science (Vol. 80, pp. 143–153).
Abstract: Dense, robust and real-time computation of depth information from stereo-camera systems is a computationally demanding requirement for robotics, advanced driver assistance systems (ADAS) and autonomous vehicles. Semi-Global Matching (SGM) is a widely used algorithm that propagates consistency constraints along several paths across the image. This work presents a real-time system producing reliable disparity estimation results on the new embedded energy-efficient GPU devices. Our design runs on a Tegra X1 at 41 frames per second for an image size of 640x480, 128 disparity levels, and using 4 path directions for the SGM method.
Keywords: Autonomous Driving; Stereo; CUDA; 3d reconstruction
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Daniel Hernandez, Alejandro Chacon, Antonio Espinosa, David Vazquez, Juan Carlos Moure, & Antonio Lopez. (2016). Stereo Matching using SGM on the GPU.
Abstract: Dense, robust and real-time computation of depth information from stereo-camera systems is a computationally demanding requirement for robotics, advanced driver assistance systems (ADAS) and autonomous vehicles. Semi-Global Matching (SGM) is a widely used algorithm that propagates consistency constraints along several paths across the image. This work presents a real-time system producing reliable disparity estimation results on the new embedded energy efficient GPU devices. Our design runs on a Tegra X1 at 42 frames per second (fps) for an image size of 640x480, 128 disparity levels, and using 4 path directions for the SGM method.
Keywords: CUDA; Stereo; Autonomous Vehicle
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Daniel Hernandez, Antonio Espinosa, David Vazquez, Antonio Lopez, & Juan Carlos Moure. (2017). Embedded Real-time Stixel Computation. In GPU Technology Conference.
Keywords: GPU; CUDA; Stixels; Autonomous Driving
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Daniel Hernandez, Antonio Espinosa, David Vazquez, Antonio Lopez, & Juan Carlos Moure. (2017). GPU-accelerated real-time stixel computation. In IEEE Winter Conference on Applications of Computer Vision (pp. 1054–1062).
Abstract: The Stixel World is a medium-level, compact representation of road scenes that abstracts millions of disparity pixels into hundreds or thousands of stixels. The goal of this work is to implement and evaluate a complete multi-stixel estimation pipeline on an embedded, energyefficient, GPU-accelerated device. This work presents a full GPU-accelerated implementation of stixel estimation that produces reliable results at 26 frames per second (real-time) on the Tegra X1 for disparity images of 1024×440 pixels and stixel widths of 5 pixels, and achieves more than 400 frames per second on a high-end Titan X GPU card.
Keywords: Autonomous Driving; GPU; Stixel
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Daniel Hernandez, Juan Carlos Moure, Toni Espinosa, Alejandro Chacon, David Vazquez, & Antonio Lopez. (2016). Real-time 3D Reconstruction for Autonomous Driving via Semi-Global Matching. In GPU Technology Conference.
Abstract: Robust and dense computation of depth information from stereo-camera systems is a computationally demanding requirement for real-time autonomous driving. Semi-Global Matching (SGM) [1] approximates heavy-computation global algorithms results but with lower computational complexity, therefore it is a good candidate for a real-time implementation. SGM minimizes energy along several 1D paths across the image. The aim of this work is to provide a real-time system producing reliable results on energy-efficient hardware. Our design runs on a NVIDIA Titan X GPU at 104.62 FPS and on a NVIDIA Drive PX at 6.7 FPS, promising for real-time platforms
Keywords: Stereo; Autonomous Driving; GPU; 3d reconstruction
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Bart M. Ter Haar Romeny, W. Niessen, J. Weickert, P. Van Roermund, W. Van Enk, Antonio Lopez, et al. (1996). Orientation detection of trabecular bone.
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Daniel Hernandez, Lukas Schneider, Antonio Espinosa, David Vazquez, Antonio Lopez, Uwe Franke, et al. (2017). Slanted Stixels: Representing San Francisco's Steepest Streets}. In 28th British Machine Vision Conference.
Abstract: In this work we present a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather restrictive geometric assumptions for Stixels by introducing a novel depth model to account for non-flat roads and slanted objects. Both semantic and depth cues are used jointly to infer the scene representation in a sound global energy minimization formulation. Furthermore, a novel approximation scheme is introduced that uses an extremely efficient over-segmentation. In doing so, the computational complexity of the Stixel inference algorithm is reduced significantly, achieving real-time computation capabilities with only a slight drop in accuracy. We evaluate the proposed approach in terms of semantic and geometric accuracy as well as run-time on four publicly available benchmark datasets. Our approach maintains accuracy on flat road scene datasets while improving substantially on a novel non-flat road dataset.
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Yu Jie, Jaume Amores, N. Sebe, Petia Radeva, & Tian Qi. (2008). Distance Learning for Similarity Estimation. IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.30(3):451–462.
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Simon Jégou, Michal Drozdzal, David Vazquez, Adriana Romero, & Yoshua Bengio. (2017). The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation. In IEEE Conference on Computer Vision and Pattern Recognition Workshops.
Abstract: State-of-the-art approaches for semantic image segmentation are built on Convolutional Neural Networks (CNNs). The typical segmentation architecture is composed of (a) a downsampling path responsible for extracting coarse semantic features, followed by (b) an upsampling path trained to recover the input image resolution at the output of the model and, optionally, (c) a post-processing module (e.g. Conditional Random Fields) to refine the model predictions.
Recently, a new CNN architecture, Densely Connected Convolutional Networks (DenseNets), has shown excellent results on image classification tasks. The idea of DenseNets is based on the observation that if each layer is directly connected to every other layer in a feed-forward fashion then the network will be more accurate and easier to train.
In this paper, we extend DenseNets to deal with the problem of semantic segmentation. We achieve state-of-the-art results on urban scene benchmark datasets such as CamVid and Gatech, without any further post-processing module nor pretraining. Moreover, due to smart construction of the model, our approach has much less parameters than currently published best entries for these datasets.
Keywords: Semantic Segmentation
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Javier Jimenez, Antonio Lopez, & Joan Serrat. (2007). Un enfoque ABP aplicado a Ingenieria del Software.
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Carme Julia, Joan Serrat, Antonio Lopez, Felipe Lumbreras, & Daniel Ponsa. (2006). Motion segmentation through factorization. Application to night driving assistance.
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Carme Julia, Angel Sappa, Felipe Lumbreras, Joan Serrat, & Antonio Lopez. (2006). Factorization with Missing and Noisy Data. In 6th International Conference on Computational Science (Vol. LNCS 3991, 555–562).
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Carme Julia, Angel Sappa, Felipe Lumbreras, Joan Serrat, & Antonio Lopez. (2006). An Iterative Multiresolution Scheme for SFM. In International Conference on Image Analysis and Recognition (Vol. LNCS 4141, 804–815).
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Carme Julia, Angel Sappa, Felipe Lumbreras, Joan Serrat, & Antonio Lopez. (2007). Motion Segmentation from Feature Trajectories with Missing Data. In J. Marti et al.(Eds.) (Ed.), 3rd. Iberian Conference on Pattern Recognition and Image Analysis (Vol. LNCS 4477, 483–490).
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