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Author |
Karel Paleček; David Geronimo; Frederic Lerasle |
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Title |
Pre-attention cues for person detection |
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Conference Article |
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Year |
2012 |
Publication |
Cognitive Behavioural Systems, COST 2102 International Training School |
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225-235 |
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Abstract |
Current state-of-the-art person detectors have been proven reliable and achieve very good detection rates. However, the performance is often far from real time, which limits their use to low resolution images only. In this paper, we deal with candidate window generation problem for person detection, i.e. we want to reduce the computational complexity of a person detector by reducing the number of regions that has to be evaluated. We base our work on Alexe’s paper [1], which introduced several pre-attention cues for generic object detection. We evaluate these cues in the context of person detection and show that their performance degrades rapidly for scenes containing multiple objects of interest such as pictures from urban environment. We extend this set by new cues, which better suits our class-specific task. The cues are designed to be simple and efficient, so that they can be used in the pre-attention phase of a more complex sliding window based person detector. |
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Dresden, Germany |
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Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
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978-3-642-34583-8 |
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COST-TS |
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no |
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Call Number |
Admin @ si @ PGL2012 |
Serial  |
2148 |
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Author |
Ferran Diego; G.D. Evangelidis; Joan Serrat |
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Title |
Night-time outdoor surveillance by mobile cameras |
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Conference Article |
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Year |
2012 |
Publication |
1st International Conference on Pattern Recognition Applications and Methods |
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2 |
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Pages |
365-371 |
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Abstract |
This paper addresses the problem of video surveillance by mobile cameras. We present a method that allows online change detection in night-time outdoor surveillance. Because of the camera movement, background frames are not available and must be “localized” in former sequences and registered with the current frames. To this end, we propose a Frame Localization And Registration (FLAR) approach that solves the problem efficiently. Frames of former sequences define a database which is queried by current frames in turn. To quickly retrieve nearest neighbors, database is indexed through a visual dictionary method based on the SURF descriptor. Furthermore, the frame localization is benefited by a temporal filter that exploits the temporal coherence of videos. Next, the recently proposed ECC alignment scheme is used to spatially register the synchronized frames. Finally, change detection methods apply to aligned frames in order to mark suspicious areas. Experiments with real night sequences recorded by in-vehicle cameras demonstrate the performance of the proposed method and verify its efficiency and effectiveness against other methods. |
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Algarve, Portugal |
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ICPRAM |
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no |
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Call Number |
Admin @ si @ DES2012 |
Serial |
2035 |
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Author |
Jose Carlos Rubio; Joan Serrat; Antonio Lopez |
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Title |
Multiple target tracking and identity linking under split, merge and occlusion of targets and observations |
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Conference Article |
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Year |
2012 |
Publication |
1st International Conference on Pattern Recognition Applications and Methods |
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Algarve, Portugal |
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ICPRAM |
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ADAS |
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no |
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Call Number |
Admin @ si @ RSL2012c; ADAS @ adas |
Serial |
2034 |
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Author |
Jose Carlos Rubio; Joan Serrat; Antonio Lopez |
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Title |
Unsupervised co-segmentation through region matching |
Type |
Conference Article |
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Year |
2012 |
Publication |
25th IEEE Conference on Computer Vision and Pattern Recognition |
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Pages |
749-756 |
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Abstract |
Co-segmentation is defined as jointly partitioning multiple images depicting the same or similar object, into foreground and background. Our method consists of a multiple-scale multiple-image generative model, which jointly estimates the foreground and background appearance distributions from several images, in a non-supervised manner. In contrast to other co-segmentation methods, our approach does not require the images to have similar foregrounds and different backgrounds to function properly. Region matching is applied to exploit inter-image information by establishing correspondences between the common objects that appear in the scene. Moreover, computing many-to-many associations of regions allow further applications, like recognition of object parts across images. We report results on iCoseg, a challenging dataset that presents extreme variability in camera viewpoint, illumination and object deformations and poses. We also show that our method is robust against large intra-class variability in the MSRC database. |
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Address |
Providence, Rhode Island |
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IEEE Xplore |
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ISSN |
1063-6919 |
ISBN |
978-1-4673-1226-4 |
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Conference |
CVPR |
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ADAS |
Approved |
no |
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Call Number |
Admin @ si @ RSL2012b; ADAS @ adas @ |
Serial |
2033 |
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Author |
Jose Carlos Rubio; Joan Serrat; Antonio Lopez; N. Paragios |
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Title |
Image Contextual Representation and Matching through Hierarchies and Higher Order Graphs |
Type |
Conference Article |
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Year |
2012 |
Publication |
21st International Conference on Pattern Recognition |
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Pages |
2664 - 2667 |
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Abstract |
We present a region matching algorithm which establishes correspondences between regions from two segmented images. An abstract graph-based representation conceals the image in a hierarchical graph, exploiting the scene properties at two levels. First, the similarity and spatial consistency of the image semantic objects is encoded in a graph of commute times. Second, the cluttered regions of the semantic objects are represented with a shape descriptor. Many-to-many matching of regions is specially challenging due to the instability of the segmentation under slight image changes, and we explicitly handle it through high order potentials. We demonstrate the matching approach applied to images of world famous buildings, captured under different conditions, showing the robustness of our method to large variations in illumination and viewpoint. |
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Address |
Tsukuba Science City, Japan |
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ISSN |
1051-4651 |
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978-1-4673-2216-4 |
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ICPR |
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ADAS |
Approved |
no |
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Call Number |
Admin @ si @ RSL2012a; |
Serial |
2032 |
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Author |
German Ros; Jesus Martinez del Rincon; Gines Garcia-Mateos |
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Title |
Articulated Particle Filter for Hand Tracking |
Type |
Conference Article |
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Year |
2012 |
Publication |
21st International Conference on Pattern Recognition |
Abbreviated Journal |
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Pages |
3581 - 3585 |
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Abstract |
This paper proposes a new version of Particle Filter, called Articulated Particle Filter – ArPF -, which has been specifically designed for an efficient sampling of hierarchical spaces, generated by articulated objects. Our approach decomposes the articulated motion into layers for efficiency purposes, making use of a careful modeling of the diffusion noise along with its propagation through the articulations. This produces an increase of accuracy and prevent for divergences. The algorithm is tested on hand tracking due to its complex hierarchical articulated nature. With this purpose, a new dataset generation tool for quantitative evaluation is also presented in this paper. |
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Tsukuba Science City, Japan |
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ISSN |
1051-4651 |
ISBN |
978-1-4673-2216-4 |
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ICPR |
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ADAS |
Approved |
no |
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Call Number |
Admin @ si @ RMG2012 |
Serial |
2031 |
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Author |
Jose Manuel Alvarez; Theo Gevers; Y. LeCun; Antonio Lopez |
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Title |
Road Scene Segmentation from a Single Image |
Type |
Conference Article |
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Year |
2012 |
Publication |
12th European Conference on Computer Vision |
Abbreviated Journal |
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Volume |
7578 |
Issue |
VII |
Pages |
376-389 |
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Keywords |
road detection |
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Abstract |
Road scene segmentation is important in computer vision for different applications such as autonomous driving and pedestrian detection. Recovering the 3D structure of road scenes provides relevant contextual information to improve their understanding.
In this paper, we use a convolutional neural network based algorithm to learn features from noisy labels to recover the 3D scene layout of a road image. The novelty of the algorithm relies on generating training labels by applying an algorithm trained on a general image dataset to classify on–board images. Further, we propose a novel texture descriptor based on a learned color plane fusion to obtain maximal uniformity in road areas. Finally, acquired (off–line) and current (on–line) information are combined to detect road areas in single images.
From quantitative and qualitative experiments, conducted on publicly available datasets, it is concluded that convolutional neural networks are suitable for learning 3D scene layout from noisy labels and provides a relative improvement of 7% compared to the baseline. Furthermore, combining color planes provides a statistical description of road areas that exhibits maximal uniformity and provides a relative improvement of 8% compared to the baseline. Finally, the improvement is even bigger when acquired and current information from a single image are combined |
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Address |
Florence, Italy |
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Springer Berlin Heidelberg |
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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-642-33785-7 |
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Conference |
ECCV |
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Notes |
ADAS;ISE |
Approved |
no |
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Call Number |
Admin @ si @ AGL2012; ADAS @ adas @ agl2012a |
Serial |
2022 |
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Author |
Miguel Oliveira; Angel Sappa; V. Santos |
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Title |
Color Correction for Onboard Multi-camera Systems using 3D Gaussian Mixture Models |
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Conference Article |
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Year |
2012 |
Publication |
IEEE Intelligent Vehicles Symposium |
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Pages |
299-303 |
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The current paper proposes a novel color correction approach for onboard multi-camera systems. It works by segmenting the given images into several regions. A probabilistic segmentation framework, using 3D Gaussian Mixture Models, is proposed. Regions are used to compute local color correction functions, which are then combined to obtain the final corrected image. An image data set of road scenarios is used to establish a performance comparison of the proposed method with other seven well known color correction algorithms. Results show that the proposed approach is the highest scoring color correction method. Also, the proposed single step 3D color space probabilistic segmentation reduces processing time over similar approaches. |
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Address |
Alcalá de Henares |
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Publisher |
IEEE Xplore |
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ISSN |
1931-0587 |
ISBN |
978-1-4673-2119-8 |
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Conference |
IV |
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Notes |
ADAS |
Approved |
no |
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Call Number |
Admin @ si @ OSS2012b |
Serial |
2021 |
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Author |
Naveen Onkarappa; Angel Sappa |
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Title |
An Empirical Study on Optical Flow Accuracy Depending on Vehicle Speed |
Type |
Conference Article |
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Year |
2012 |
Publication |
IEEE Intelligent Vehicles Symposium |
Abbreviated Journal |
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1138-1143 |
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Abstract |
Driver assistance and safety systems are getting attention nowadays towards automatic navigation and safety. Optical flow as a motion estimation technique has got major roll in making these systems a reality. Towards this, in the current paper, the suitability of polar representation for optical flow estimation in such systems is demonstrated. Furthermore, the influence of individual regularization terms on the accuracy of optical flow on image sequences of different speeds is empirically evaluated. Also a new synthetic dataset of image sequences with different speeds is generated along with the ground-truth optical flow. |
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Address |
Alcalá de Henares |
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IEEE Xplore |
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1931-0587 |
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978-1-4673-2119-8 |
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IV |
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ADAS |
Approved |
no |
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Call Number |
Admin @ si @ NaS2012 |
Serial |
2020 |
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Author |
German Ros; Angel Sappa; Daniel Ponsa; Antonio Lopez |
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Title |
Visual SLAM for Driverless Cars: A Brief Survey |
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Conference Article |
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Year |
2012 |
Publication |
IEEE Workshop on Navigation, Perception, Accurate Positioning and Mapping for Intelligent Vehicles |
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SLAM |
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Address |
Alcalá de Henares |
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IVW |
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ADAS |
Approved |
no |
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Call Number |
Admin @ si @ RSP2012; ADAS @ adas |
Serial |
2019 |
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