2,566 research outputs found

    GPU-based pedestrian detection for autonomous driving

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    Pedestrian detection has gained a lot of prominence during the last few years. Besides the fact that it is one of the hardest tasks within computer vision, it involves huge computational costs. Obtaining acceptable real-time performance, measured in frames per second (fps), for the most advanced algorithms is nowadays a hard challenge. In this work, we propose a GPU implementation of a well-known pedestrian detection system (i.e., HOGLBP-SVM) specially designed for the Tegra X1 embedded GPU. It includes LBP and HOG as feature descriptors and SVM as classifiers. We introduce significant algorithmic adjustments and optimizations to adapt the problem to the NVIDIA GPU architecture without sacrificing accuracy. The aim of this work is to offer a real-time system providing reliable results.La detecció de vianants ha estat un tema de molt interès els darrers anys. A part de ser una de les tasques més complexes de la visió per computador, implica uns costos computacionals molt elevats. Obtenir un rendiment de temps real acceptable, mesurat en imatges processades per segon (fps), per la majoria d'algoritmes més avançats és una fita complicada. Aquest treball proposa una implementació en GPU d'un conegut detector de vianants (i.e., HOGLBP-SVM) dissenyat expressament per la Tegra X1, una GPU encastada. El detector inclou els mètodes LBP i HOG com descriptors de característiques i un SVM com a classificador. El sistema introdueix ajustos algorítmics i optimitzacions per adaptar el problema a l'arquitectura d'una GPU NVIDIA sense sacrificar precisió. L'objectiu és proporcionar un sistema de temps real que alhora sigui robust.La detección de peatones ha ganado mucho interés en los últimos años. A parte de ser una de las tareas más complejas dentro la visión por computador, esta implica unos costes computacionales muy elevados. Obtener un rendimiento de tiempo real aceptable, medido en imágenes procesadas por segundo (fps), para la mayoría de algoritmos más avanzados es un hito complicado. Este trabajo propone una implementación en GPU de un conocido detector de peatones (i.e., HOGLBP-SVM) diseñado para la Tegra X1, una GPU embebida. El detector incluye los metodos LBP i HOG como descriptores de características i un SVM como clasificador. El sistema introduce ajustes algorítmicos i optimizaciones para adaptar el problema a la arquitectura de una GPU NVIDIA sin sacrificar precisión. El objetivo es proporcionar un sistema de tiempo real que a la vez sea robusto

    Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection

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    Effective fusion of complementary information captured by multi-modal sensors (visible and infrared cameras) enables robust pedestrian detection under various surveillance situations (e.g. daytime and nighttime). In this paper, we present a novel box-level segmentation supervised learning framework for accurate and real-time multispectral pedestrian detection by incorporating features extracted in visible and infrared channels. Specifically, our method takes pairs of aligned visible and infrared images with easily obtained bounding box annotations as input and estimates accurate prediction maps to highlight the existence of pedestrians. It offers two major advantages over the existing anchor box based multispectral detection methods. Firstly, it overcomes the hyperparameter setting problem occurred during the training phase of anchor box based detectors and can obtain more accurate detection results, especially for small and occluded pedestrian instances. Secondly, it is capable of generating accurate detection results using small-size input images, leading to improvement of computational efficiency for real-time autonomous driving applications. Experimental results on KAIST multispectral dataset show that our proposed method outperforms state-of-the-art approaches in terms of both accuracy and speed

    Joint 3D Proposal Generation and Object Detection from View Aggregation

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    We present AVOD, an Aggregate View Object Detection network for autonomous driving scenarios. The proposed neural network architecture uses LIDAR point clouds and RGB images to generate features that are shared by two subnetworks: a region proposal network (RPN) and a second stage detector network. The proposed RPN uses a novel architecture capable of performing multimodal feature fusion on high resolution feature maps to generate reliable 3D object proposals for multiple object classes in road scenes. Using these proposals, the second stage detection network performs accurate oriented 3D bounding box regression and category classification to predict the extents, orientation, and classification of objects in 3D space. Our proposed architecture is shown to produce state of the art results on the KITTI 3D object detection benchmark while running in real time with a low memory footprint, making it a suitable candidate for deployment on autonomous vehicles. Code is at: https://github.com/kujason/avodComment: For any inquiries contact aharakeh(at)uwaterloo(dot)c
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