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Author |
Francisco Jose Perales; Juan J. Villanueva; Yuhua Luo |
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An automatic two-camera human motion perception system based on biomechanical model matching |
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Conference Article |
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1991 |
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IEEE International Conference on Systems, Man and Cybernetics |
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2 |
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856-858 |
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ISE @ ise @ PVL1991b |
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265 |
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Vishwesh Pillai; Pranav Mehar; Manisha Das; Deep Gupta; Petia Radeva |
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Title |
Integrated Hierarchical and Flat Classifiers for Food Image Classification using Epistemic Uncertainty |
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2022 |
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IEEE International Conference on Signal Processing and Communications |
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The problem of food image recognition is an essential one in today’s context because health conditions such as diabetes, obesity, and heart disease require constant monitoring of a person’s diet. To automate this process, several models are available to recognize food images. Due to a considerable number of unique food dishes and various cuisines, a traditional flat classifier ceases to perform well. To address this issue, prediction schemes consisting of both flat and hierarchical classifiers, with the analysis of epistemic uncertainty are used to switch between the classifiers. However, the accuracy of the predictions made using epistemic uncertainty data remains considerably low. Therefore, this paper presents a prediction scheme using three different threshold criteria that helps to increase the accuracy of epistemic uncertainty predictions. The performance of the proposed method is demonstrated using several experiments performed on the MAFood-121 dataset. The experimental results validate the proposal performance and show that the proposed threshold criteria help to increase the overall accuracy of the predictions by correctly classifying the uncertainty distribution of the samples. |
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Bangalore; India; July 2022 |
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SPCOM |
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MILAB; no menciona |
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no |
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Admin @ si @ PMD2022 |
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3796 |
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Author |
Zhong Jin; Franck Davoine; Zhen Lou |
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Title |
Facial expression analysis by using KPCA |
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Miscellaneous |
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2003 |
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IEEE International Conference on Robotics, Intelligent Systems and Signal Processing (IEEE RISSP 2003), pp736–741 |
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Changsha, Hunan, China |
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Admin @ si @ JDL2003 |
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431 |
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Author |
T. Alejandra Vidal; Andrew J. Davison; Juan Andrade; David W. Murray |
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Title |
Active Control for Single Camera SLAM |
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Miscellaneous |
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2006 |
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IEEE International Conference on Robotics and Automation, 1930–1936 |
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Orlando (Florida) |
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DAG @ dag @ VDA2006 |
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666 |
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Author |
Fadi Dornaika; Bogdan Raducanu |
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Title |
Detecting and Tracking of 3D Face Pose for Human-Robot Interaction |
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Conference Article |
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2008 |
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IEEE International Conference on Robotics and Automation, |
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1716–1721 |
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Pasadena; CA; USA |
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ICRA |
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OR;MV |
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no |
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BCNPCL @ bcnpcl @ DoR2008a |
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982 |
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Author |
Arnau Ramisa; Adriana Tapus; Ramon Lopez de Mantaras; Ricardo Toledo |
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Title |
Mobile Robot Localization using Panoramic Vision and Combination of Feature Region Detectors |
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Conference Article |
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2008 |
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IEEE International Conference on Robotics and Automation, |
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538–543 |
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Pasadena; CA; USA |
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RV;ADAS |
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no |
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Admin @ si @ RTL2008 |
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1144 |
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Author |
Alloy Das; Sanket Biswas; Umapada Pal; Josep Llados |
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Title |
Diving into the Depths of Spotting Text in Multi-Domain Noisy Scenes |
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Conference Article |
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2024 |
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IEEE International Conference on Robotics and Automation in PACIFICO |
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When used in a real-world noisy environment, the capacity to generalize to multiple domains is essential for any autonomous scene text spotting system. However, existing state-of-the-art methods employ pretraining and fine-tuning strategies on natural scene datasets, which do not exploit the feature interaction across other complex domains. In this work, we explore and investigate the problem of domain-agnostic scene text spotting, i.e., training a model on multi-domain source data such that it can directly generalize to target domains rather than being specialized for a specific domain or scenario. In this regard, we present the community a text spotting validation benchmark called Under-Water Text (UWT) for noisy underwater scenes to establish an important case study. Moreover, we also design an efficient super-resolution based end-to-end transformer baseline called DA-TextSpotter which achieves comparable or superior performance over existing text spotting architectures for both regular and arbitrary-shaped scene text spotting benchmarks in terms of both accuracy and model efficiency. The dataset, code and pre-trained models will be released upon acceptance. |
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Yokohama; Japan; May 2024 |
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DAG |
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no |
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Admin @ si @ DBP2024 |
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3979 |
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Author |
Angel Sappa |
![goto web page url](img/www.gif)
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Title |
Efficient Closed Contour Extraction from Range Image Edge Points |
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Miscellaneous |
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2005 |
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IEEE International Conference on Robotics and Automation |
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Barcelona (Spain) |
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ADAS @ adas @ Sap2005 |
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538 |
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Author |
Elvina Motard; Bogdan Raducanu; Viviane Cadenat; Jordi Vitria |
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Title |
Incremental On-Line Topological Map Learning for A Visual Homing Application |
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Conference Article |
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2007 |
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IEEE International Conference on Robotics and Automation |
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2049–2054 |
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Roma (Italy) |
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OR; MV |
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no |
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BCNPCL @ bcnpcl @ MRC2007 |
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793 |
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Author |
Hugo Berti; Angel Sappa; Osvaldo Agamennoni |
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Title |
Autonomous robot navigation with a global and asymptotic convergence |
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Conference Article |
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2007 |
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IEEE International Conference on Robotics and Automation |
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2712–2717 |
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Roma (Italy) |
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ADAS |
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ADAS @ adas @ BSA2007 |
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796 |
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Author |
Bogdan Raducanu; Fadi Dornaika |
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Title |
Dynamic Facial Expression Recognition Using Laplacian Eigenmaps-Based Manifold Learning |
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Conference Article |
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2010 |
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IEEE International Conference on Robotics and Automation |
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156–161 |
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In this paper, we propose an integrated framework for tracking, modelling and recognition of facial expressions. The main contributions are: (i) a view- and texture independent scheme that exploits facial action parameters estimated by an appearance-based 3D face tracker; (ii) the complexity of the non-linear facial expression space is modelled through a manifold, whose structure is learned using Laplacian Eigenmaps. The projected facial expressions are afterwards recognized based on Nearest Neighbor classifier; (iii) with the proposed approach, we developed an application for an AIBO robot, in which it mirrors the perceived facial expression. |
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Anchorage; AK; USA; |
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1050-4729 |
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978-1-4244-5038-1 |
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OR; MV |
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BCNPCL @ bcnpcl @ RaD2010 |
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1310 |
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Author |
Jiaolong Xu; David Vazquez; Krystian Mikolajczyk; Antonio Lopez |
![download PDF file pdf](img/file_PDF.gif)
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Title |
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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ADAS; 600.085; 600.082; 600.076 |
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Admin @ si @ XVM2016 |
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2728 |
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Felipe Codevilla; Matthias Muller; Antonio Lopez; Vladlen Koltun; Alexey Dosovitskiy |
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Title |
End-to-end Driving via Conditional Imitation Learning |
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Conference Article |
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2018 |
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IEEE International Conference on Robotics and Automation |
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4693 - 4700 |
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Deep networks trained on demonstrations of human driving have learned to follow roads and avoid obstacles. However, driving policies trained via imitation learning cannot be controlled at test time. A vehicle trained end-to-end to imitate an expert cannot be guided to take a specific turn at an upcoming intersection. This limits the utility of such systems. We propose to condition imitation learning on high-level command input. At test time, the learned driving policy functions as a chauffeur that handles sensorimotor coordination but continues to respond to navigational commands. We evaluate different architectures for conditional imitation learning in vision-based driving. We conduct experiments in realistic three-dimensional simulations of urban driving and on a 1/5 scale robotic truck that is trained to drive in a residential area. Both systems drive based on visual input yet remain responsive to high-level navigational commands. The supplementary video can be viewed at this https URL |
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Brisbane; Australia; May 2018 |
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ADAS; 600.116; 600.124; 600.118 |
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Admin @ si @ CML2018 |
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3108 |
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Jiaolong Xu; Peng Wang; Heng Yang; Antonio Lopez |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Training a Binary Weight Object Detector by Knowledge Transfer for Autonomous Driving |
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Conference Article |
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2019 |
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IEEE International Conference on Robotics and Automation |
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2379-2384 |
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Autonomous driving has harsh requirements of small model size and energy efficiency, in order to enable the embedded system to achieve real-time on-board object detection. Recent deep convolutional neural network based object detectors have achieved state-of-the-art accuracy. However, such models are trained with numerous parameters and their high computational costs and large storage prohibit the deployment to memory and computation resource limited systems. Low-precision neural networks are popular techniques for reducing the computation requirements and memory footprint. Among them, binary weight neural network (BWN) is the extreme case which quantizes the float-point into just bit. BWNs are difficult to train and suffer from accuracy deprecation due to the extreme low-bit representation. To address this problem, we propose a knowledge transfer (KT) method to aid the training of BWN using a full-precision teacher network. We built DarkNet-and MobileNet-based binary weight YOLO-v2 detectors and conduct experiments on KITTI benchmark for car, pedestrian and cyclist detection. The experimental results show that the proposed method maintains high detection accuracy while reducing the model size of DarkNet-YOLO from 257 MB to 8.8 MB and MobileNet-YOLO from 193 MB to 7.9 MB. |
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Montreal; Canada; May 2019 |
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Sangeeth Reddy; Minesh Mathew; Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas; C.V. Jawahar |
![download PDF file pdf](img/file_PDF.gif)
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RoadText-1K: Text Detection and Recognition Dataset for Driving Videos |
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2020 |
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IEEE International Conference on Robotics and Automation |
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Perceiving text is crucial to understand semantics of outdoor scenes and hence is a critical requirement to build intelligent systems for driver assistance and self-driving. Most of the existing datasets for text detection and recognition comprise still images and are mostly compiled keeping text in mind. This paper introduces a new ”RoadText-1K” dataset for text in driving videos. The dataset is 20 times larger than the existing largest dataset for text in videos. Our dataset comprises 1000 video clips of driving without any bias towards text and with annotations for text bounding boxes and transcriptions in every frame. State of the art methods for text detection,
recognition and tracking are evaluated on the new dataset and the results signify the challenges in unconstrained driving videos compared to existing datasets. This suggests that RoadText-1K is suited for research and development of reading systems, robust enough to be incorporated into more complex downstream tasks like driver assistance and self-driving. The dataset can be found at http://cvit.iiit.ac.in/research/
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Paris; Francia; ??? |
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DAG; 600.121; 600.129 |
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Admin @ si @ RMG2020 |
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3400 |
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