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Victor Campmany; Sergio Silva; Antonio Espinosa; Juan Carlos Moure; David Vazquez; Antonio Lopez |
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Title |
GPU-based pedestrian detection for autonomous driving |
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Conference Article |
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2016 |
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16th International Conference on Computational Science |
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80 |
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2377-2381 |
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Pedestrian detection; Autonomous Driving; CUDA |
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We propose a real-time pedestrian detection system for the embedded Nvidia Tegra X1 GPU-CPU hybrid platform. The pipeline is composed by the following state-of-the-art algorithms: Histogram of Local Binary Patterns (LBP) and Histograms of Oriented Gradients (HOG) features extracted from the input image; Pyramidal Sliding Window technique for foreground segmentation; and Support Vector Machine (SVM) for classification. Results show a 8x speedup in the target Tegra X1 platform and a better performance/watt ratio than desktop CUDA platforms in study. |
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San Diego; CA; USA; June 2016 |
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ADAS; 600.085; 600.082; 600.076 |
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ADAS @ adas @ CSE2016 |
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2741 |
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David Aldavert; Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo |
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Title |
Fast and Robust Object Segmentation with the Integral Linear Classifier |
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2010 |
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23rd IEEE Conference on Computer Vision and Pattern Recognition |
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1046–1053 |
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We propose an efficient method, built on the popular Bag of Features approach, that obtains robust multiclass pixel-level object segmentation of an image in less than 500ms, with results comparable or better than most state of the art methods. We introduce the Integral Linear Classifier (ILC), that can readily obtain the classification score for any image sub-window with only 6 additions and 1 product by fusing the accumulation and classification steps in a single operation. In order to design a method as efficient as possible, our building blocks are carefully selected from the quickest in the state of the art. More precisely, we evaluate the performance of three popular local descriptors, that can be very efficiently computed using integral images, and two fast quantization methods: the Hierarchical K-Means, and the Extremely Randomized Forest. Finally, we explore the utility of adding spatial bins to the Bag of Features histograms and that of cascade classifiers to improve the obtained segmentation. Our method is compared to the state of the art in the difficult Graz-02 and PASCAL 2007 Segmentation Challenge datasets. |
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San Francisco; CA; USA; June 2010 |
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1063-6919 |
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978-1-4244-6984-0 |
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CVPR |
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ADAS |
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Admin @ si @ ARL2010a |
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1311 |
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Jiaolong Xu; David Vazquez; Krystian Mikolajczyk; Antonio Lopez |
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Hierarchical online domain adaptation of deformable part-based models |
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2016 |
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IEEE International Conference on Robotics and Automation |
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5536-5541 |
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Domain Adaptation; Pedestrian Detection |
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We propose an online domain adaptation method for the deformable part-based model (DPM). The online domain adaptation is based on a two-level hierarchical adaptation tree, which consists of instance detectors in the leaf nodes and a category detector at the root node. Moreover, combined with a multiple object tracking procedure (MOT), our proposal neither requires target-domain annotated data nor revisiting the source-domain data for performing the source-to-target domain adaptation of the DPM. From a practical point of view this means that, given a source-domain DPM and new video for training on a new domain without object annotations, our procedure outputs a new DPM adapted to the domain represented by the video. As proof-of-concept we apply our proposal to the challenging task of pedestrian detection. In this case, each instance detector is an exemplar classifier trained online with only one pedestrian per frame. The pedestrian instances are collected by MOT and the hierarchical model is constructed dynamically according to the pedestrian trajectories. Our experimental results show that the adapted detector achieves the accuracy of recent supervised domain adaptation methods (i.e., requiring manually annotated targetdomain data), and improves the source detector more than 10 percentage points. |
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Stockholm; Sweden; May 2016 |
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ICRA |
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ADAS; 600.085; 600.082; 600.076 |
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Admin @ si @ XVM2016 |
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2728 |
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Author |
Marc Masana; Idoia Ruiz; Joan Serrat; Joost Van de Weijer; Antonio Lopez |
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Title |
Metric Learning for Novelty and Anomaly Detection |
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2018 |
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29th British Machine Vision Conference |
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When neural networks process images which do not resemble the distribution seen during training, so called out-of-distribution images, they often make wrong predictions, and do so too confidently. The capability to detect out-of-distribution images is therefore crucial for many real-world applications. We divide out-of-distribution detection between novelty detection ---images of classes which are not in the training set but are related to those---, and anomaly detection ---images with classes which are unrelated to the training set. By related we mean they contain the same type of objects, like digits in MNIST and SVHN. Most existing work has focused on anomaly detection, and has addressed this problem considering networks trained with the cross-entropy loss. Differently from them, we propose to use metric learning which does not have the drawback of the softmax layer (inherent to cross-entropy methods), which forces the network to divide its prediction power over the learned classes. We perform extensive experiments and evaluate both novelty and anomaly detection, even in a relevant application such as traffic sign recognition, obtaining comparable or better results than previous works. |
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Newcastle; uk; September 2018 |
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BMVC |
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LAMP; ADAS; 601.305; 600.124; 600.106; 602.200; 600.120; 600.118 |
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Admin @ si @ MRS2018 |
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3156 |
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