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Jose Carlos Rubio, Joan Serrat and Antonio Lopez. 2012. Multiple target tracking and identity linking under split, merge and occlusion of targets and observations. 1st International Conference on Pattern Recognition Applications and Methods.
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Ferran Diego, G.D. Evangelidis and Joan Serrat. 2012. Night-time outdoor surveillance by mobile cameras. 1st International Conference on Pattern Recognition Applications and Methods.365–371.
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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Karel Paleček, David Geronimo and Frederic Lerasle. 2012. Pre-attention cues for person detection. Cognitive Behavioural Systems, COST 2102 International Training School. Springer Berlin Heidelberg, 225–235. (LNCS.)
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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Jose Carlos Rubio, Joan Serrat and Antonio Lopez. 2012. Video Co-segmentation. 11th Asian Conference on Computer Vision. Springer Berlin Heidelberg, 13–24. (LNCS.)
Abstract: Segmentation of a single image is in general a highly underconstrained problem. A frequent approach to solve it is to somehow provide prior knowledge or constraints on how the objects of interest look like (in terms of their shape, size, color, location or structure). Image co-segmentation trades the need for such knowledge for something much easier to obtain, namely, additional images showing the object from other viewpoints. Now the segmentation problem is posed as one of differentiating the similar object regions in all the images from the more varying background. In this paper, for the first time, we extend this approach to video segmentation: given two or more video sequences showing the same object (or objects belonging to the same class) moving in a similar manner, we aim to outline its region in all the frames. In addition, the method works in an unsupervised manner, by learning to segment at testing time. We compare favorably with two state-of-the-art methods on video segmentation and report results on benchmark videos.
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Monica Piñol, Angel Sappa and Ricardo Toledo. 2012. MultiTable Reinforcement for Visual Object Recognition. 4th International Conference on Signal and Image Processing. Springer India, 469–480. (LNCS.)
Abstract: This paper presents a bag of feature based method for visual object recognition. Our contribution is focussed on the selection of the best feature descriptor. It is implemented by using a novel multi-table reinforcement learning method that selects among five of classical descriptors (i.e., Spin, SIFT, SURF, C-SIFT and PHOW) the one that best describes each image. Experimental results and comparisons are provided showing the improvements achieved with the proposed approach.
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Mohammad Rouhani and Angel Sappa. 2012. Non-Rigid Shape Registration: A Single Linear Least Squares Framework. 12th European Conference on Computer Vision. Springer Berlin Heidelberg, 264–277. (LNCS.)
Abstract: This paper proposes a non-rigid registration formulation capturing both global and local deformations in a single framework. This formulation is based on a quadratic estimation of the registration distance together with a quadratic regularization term. Hence, the optimal transformation parameters are easily obtained by solving a liner system of equations, which guarantee a fast convergence. Experimental results with challenging 2D and 3D shapes are presented to show the validity of the proposed framework. Furthermore, comparisons with the most relevant approaches are provided.
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Miguel Oliveira, V.Santos and Angel Sappa. 2012. Short term path planning using a multiple hypothesis evaluation approach for an autonomous driving competition. IEEE 4th Workshop on Planning, Perception and Navigation for Intelligent Vehicles.
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Katerine Diaz, Francesc J. Ferri and W. Diaz. 2013. Fast Approximated Discriminative Common Vectors using rank-one SVD updates. 20th International Conference On Neural Information Processing. Springer Berlin Heidelberg, 368–375. (LNCS.)
Abstract: An efficient incremental approach to the discriminative common vector (DCV) method for dimensionality reduction and classification is presented. The proposal consists of a rank-one update along with an adaptive restriction on the rank of the null space which leads to an approximate but convenient solution. The algorithm can be implemented very efficiently in terms of matrix operations and space complexity, which enables its use in large-scale dynamic application domains. Deep comparative experimentation using publicly available high dimensional image datasets has been carried out in order to properly assess the proposed algorithm against several recent incremental formulations.
K. Diaz-Chito, F.J. Ferri, W. Diaz
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Jiaolong Xu, Sebastian Ramos, David Vazquez and Antonio Lopez. 2013. DA-DPM Pedestrian Detection. ICCV Workshop on Reconstruction meets Recognition.
Keywords: Domain Adaptation; Pedestrian Detection
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Yi Xiao, Felipe Codevilla, Diego Porres and Antonio Lopez. 2023. Scaling Vision-Based End-to-End Autonomous Driving with Multi-View Attention Learning. International Conference on Intelligent Robots and Systems.
Abstract: On end-to-end driving, human driving demonstrations are used to train perception-based driving models by imitation learning. This process is supervised on vehicle signals (e.g., steering angle, acceleration) but does not require extra costly supervision (human labeling of sensor data). As a representative of such vision-based end-to-end driving models, CILRS is commonly used as a baseline to compare with new driving models. So far, some latest models achieve better performance than CILRS by using expensive sensor suites and/or by using large amounts of human-labeled data for training. Given the difference in performance, one may think that it is not worth pursuing vision-based pure end-to-end driving. However, we argue that this approach still has great value and potential considering cost and maintenance. In this paper, we present CIL++, which improves on CILRS by both processing higher-resolution images using a human-inspired HFOV as an inductive bias and incorporating a proper attention mechanism. CIL++ achieves competitive performance compared to models which are more costly to develop. We propose to replace CILRS with CIL++ as a strong vision-based pure end-to-end driving baseline supervised by only vehicle signals and trained by conditional imitation learning.
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