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Author |
Javier Marin; David Vazquez; Antonio Lopez; Jaume Amores; Ludmila I. Kuncheva |
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Title |
Occlusion handling via random subspace classifiers for human detection |
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Year |
2014 |
Publication |
IEEE Transactions on Systems, Man, and Cybernetics (Part B) |
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TSMCB |
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44 |
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3 |
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342-354 |
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Keywords |
Pedestriand Detection; occlusion handling |
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Abstract |
This paper describes a general method to address partial occlusions for human detection in still images. The Random Subspace Method (RSM) is chosen for building a classifier ensemble robust against partial occlusions. The component classifiers are chosen on the basis of their individual and combined performance. The main contribution of this work lies in our approach’s capability to improve the detection rate when partial occlusions are present without compromising the detection performance on non occluded data. In contrast to many recent approaches, we propose a method which does not require manual labelling of body parts, defining any semantic spatial components, or using additional data coming from motion or stereo. Moreover, the method can be easily extended to other object classes. The experiments are performed on three large datasets: the INRIA person dataset, the Daimler Multicue dataset, and a new challenging dataset, called PobleSec, in which a considerable number of targets are partially occluded. The different approaches are evaluated at the classification and detection levels for both partially occluded and non-occluded data. The experimental results show that our detector outperforms state-of-the-art approaches in the presence of partial occlusions, while offering performance and reliability similar to those of the holistic approach on non-occluded data. The datasets used in our experiments have been made publicly available for benchmarking purposes |
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2168-2267 |
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ADAS; 605.203; 600.057; 600.054; 601.042; 601.187; 600.076 |
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ADAS @ adas @ MVL2014 |
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2213 |
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Author |
Jose Manuel Alvarez; Antonio Lopez; Theo Gevers; Felipe Lumbreras |
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Title |
Combining Priors, Appearance and Context for Road Detection |
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Journal Article |
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Year |
2014 |
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IEEE Transactions on Intelligent Transportation Systems |
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TITS |
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15 |
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3 |
Pages |
1168-1178 |
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Illuminant invariance; lane markings; road detection; road prior; road scene understanding; vanishing point; 3-D scene layout |
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Abstract |
Detecting the free road surface ahead of a moving vehicle is an important research topic in different areas of computer vision, such as autonomous driving or car collision warning.
Current vision-based road detection methods are usually based solely on low-level features. Furthermore, they generally assume structured roads, road homogeneity, and uniform lighting conditions, constraining their applicability in real-world scenarios. In this paper, road priors and contextual information are introduced for road detection. First, we propose an algorithm to estimate road priors online using geographical information, providing relevant initial information about the road location. Then, contextual cues, including horizon lines, vanishing points, lane markings, 3-D scene layout, and road geometry, are used in addition to low-level cues derived from the appearance of roads. Finally, a generative model is used to combine these cues and priors, leading to a road detection method that is, to a large degree, robust to varying imaging conditions, road types, and scenarios. |
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IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
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1524-9050 |
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ADAS; 600.076;ISE |
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Admin @ si @ ALG2014 |
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2501 |
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T. Mouats; N. Aouf; Angel Sappa; Cristhian A. Aguilera-Carrasco; Ricardo Toledo |
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Title |
Multi-Spectral Stereo Odometry |
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Journal Article |
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2015 |
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IEEE Transactions on Intelligent Transportation Systems |
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TITS |
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16 |
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3 |
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1210-1224 |
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Egomotion estimation; feature matching; multispectral odometry (MO); optical flow; stereo odometry; thermal imagery |
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In this paper, we investigate the problem of visual odometry for ground vehicles based on the simultaneous utilization of multispectral cameras. It encompasses a stereo rig composed of an optical (visible) and thermal sensors. The novelty resides in the localization of the cameras as a stereo setup rather
than two monocular cameras of different spectrums. To the best of our knowledge, this is the first time such task is attempted. Log-Gabor wavelets at different orientations and scales are used to extract interest points from both images. These are then described using a combination of frequency and spatial information within the local neighborhood. Matches between the pairs of multimodal images are computed using the cosine similarity function based
on the descriptors. Pyramidal Lucas–Kanade tracker is also introduced to tackle temporal feature matching within challenging sequences of the data sets. The vehicle egomotion is computed from the triangulated 3-D points corresponding to the matched features. A windowed version of bundle adjustment incorporating
Gauss–Newton optimization is utilized for motion estimation. An outlier removal scheme is also included within the framework to deal with outliers. Multispectral data sets were generated and used as test bed. They correspond to real outdoor scenarios captured using our multimodal setup. Finally, detailed results validating the proposed strategy are illustrated. |
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1524-9050 |
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ADAS; 600.055; 600.076 |
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Admin @ si @ MAS2015a |
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2533 |
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Author |
David Aldavert; Marçal Rusiñol; Ricardo Toledo; Josep Llados |
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Title |
A Study of Bag-of-Visual-Words Representations for Handwritten Keyword Spotting |
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2015 |
Publication |
International Journal on Document Analysis and Recognition |
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IJDAR |
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18 |
Issue |
3 |
Pages |
223-234 |
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Bag-of-Visual-Words; Keyword spotting; Handwritten documents; Performance evaluation |
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The Bag-of-Visual-Words (BoVW) framework has gained popularity among the document image analysis community, specifically as a representation of handwritten words for recognition or spotting purposes. Although in the computer vision field the BoVW method has been greatly improved, most of the approaches in the document image analysis domain still rely on the basic implementation of the BoVW method disregarding such latest refinements. In this paper, we present a review of those improvements and its application to the keyword spotting task. We thoroughly evaluate their impact against a baseline system in the well-known George Washington dataset and compare the obtained results against nine state-of-the-art keyword spotting methods. In addition, we also compare both the baseline and improved systems with the methods presented at the Handwritten Keyword Spotting Competition 2014. |
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Springer Berlin Heidelberg |
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1433-2833 |
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DAG; ADAS; 600.055; 600.061; 601.223; 600.077; 600.097 |
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Call Number |
Admin @ si @ ART2015 |
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2679 |
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Author |
Katerine Diaz; Jesus Martinez del Rincon; Marçal Rusiñol; Aura Hernandez-Sabate |
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Title |
Feature Extraction by Using Dual-Generalized Discriminative Common Vectors |
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Journal Article |
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Year |
2019 |
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Journal of Mathematical Imaging and Vision |
Abbreviated Journal |
JMIV |
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Volume |
61 |
Issue |
3 |
Pages |
331-351 |
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Keywords |
Online feature extraction; Generalized discriminative common vectors; Dual learning; Incremental learning; Decremental learning |
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In this paper, a dual online subspace-based learning method called dual-generalized discriminative common vectors (Dual-GDCV) is presented. The method extends incremental GDCV by exploiting simultaneously both the concepts of incremental and decremental learning for supervised feature extraction and classification. Our methodology is able to update the feature representation space without recalculating the full projection or accessing the previously processed training data. It allows both adding information and removing unnecessary data from a knowledge base in an efficient way, while retaining the previously acquired knowledge. The proposed method has been theoretically proved and empirically validated in six standard face recognition and classification datasets, under two scenarios: (1) removing and adding samples of existent classes, and (2) removing and adding new classes to a classification problem. Results show a considerable computational gain without compromising the accuracy of the model in comparison with both batch methodologies and other state-of-art adaptive methods. |
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DAG; ADAS; 600.084; 600.118; 600.121; 600.129 |
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Admin @ si @ DRR2019 |
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3172 |
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