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Jose Manuel Alvarez, Theo Gevers, Ferran Diego, & Antonio Lopez. (2013). Road Geometry Classification by Adaptative Shape Models. TITS - IEEE Transactions on Intelligent Transportation Systems, 14(1), 459–468.
Abstract: Vision-based road detection is important for different applications in transportation, such as autonomous driving, vehicle collision warning, and pedestrian crossing detection. Common approaches to road detection are based on low-level road appearance (e.g., color or texture) and neglect of the scene geometry and context. Hence, using only low-level features makes these algorithms highly depend on structured roads, road homogeneity, and lighting conditions. Therefore, the aim of this paper is to classify road geometries for road detection through the analysis of scene composition and temporal coherence. Road geometry classification is proposed by building corresponding models from training images containing prototypical road geometries. We propose adaptive shape models where spatial pyramids are steered by the inherent spatial structure of road images. To reduce the influence of lighting variations, invariant features are used. Large-scale experiments show that the proposed road geometry classifier yields a high recognition rate of 73.57% ± 13.1, clearly outperforming other state-of-the-art methods. Including road shape information improves road detection results over existing appearance-based methods. Finally, it is shown that invariant features and temporal information provide robustness against disturbing imaging conditions.
Keywords: road detection
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Pau Baiget, Carles Fernandez, Xavier Roca, & Jordi Gonzalez. (2009). Generation of Augmented Video Sequences Combining Behavioral Animation and Multi Object Tracking. Computer Animation and Virtual Worlds, 20(4), 473–489.
Abstract: In this paper we present a novel approach to generate augmented video sequences in real-time, involving interactions between virtual and real agents in real scenarios. On the one hand, real agent motion is estimated by means of a multi-object tracking algorithm, which determines real objects' position over the scenario for each time step. On the other hand, virtual agents are provided with behavior models considering their interaction with the environment and with other agents. The resulting framework allows to generate video sequences involving behavior-based virtual agents that react to real agent behavior and has applications in education, simulation, and in the game and movie industries. We show the performance of the proposed approach in an indoor and outdoor scenario simulating human and vehicle agents. Copyright © 2009 John Wiley & Sons, Ltd.
We present a novel approach to generate augmented video sequences in real-time, involving interactions between virtual and real agents in real scenarios. On the one hand, real agent motion is estimated by means of a multi-object tracking algorithm, which determines real objects' position over the scenario for each time step. On the other hand, virtual agents are provided with behavior models considering their interaction with the environment and with other agents. © 2009 Wiley Periodicals, Inc.
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Noha Elfiky, Jordi Gonzalez, & Xavier Roca. (2012). Compact and Adaptive Spatial Pyramids for Scene Recognition. IMAVIS - Image and Vision Computing, 30(8), 492–500.
Abstract: Most successful approaches on scenerecognition tend to efficiently combine global image features with spatial local appearance and shape cues. On the other hand, less attention has been devoted for studying spatial texture features within scenes. Our method is based on the insight that scenes can be seen as a composition of micro-texture patterns. This paper analyzes the role of texture along with its spatial layout for scenerecognition. However, one main drawback of the resulting spatial representation is its huge dimensionality. Hence, we propose a technique that addresses this problem by presenting a compactSpatialPyramid (SP) representation. The basis of our compact representation, namely, CompactAdaptiveSpatialPyramid (CASP) consists of a two-stages compression strategy. This strategy is based on the Agglomerative Information Bottleneck (AIB) theory for (i) compressing the least informative SP features, and, (ii) automatically learning the most appropriate shape for each category. Our method exceeds the state-of-the-art results on several challenging scenerecognition data sets.
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Pau Rodriguez, Diego Velazquez, Guillem Cucurull, Josep M. Gonfaus, Xavier Roca, & Jordi Gonzalez. (2020). Pay attention to the activations: a modular attention mechanism for fine-grained image recognition. TMM - IEEE Transactions on Multimedia, 22(2), 502–514.
Abstract: Fine-grained image recognition is central to many multimedia tasks such as search, retrieval, and captioning. Unfortunately, these tasks are still challenging since the appearance of samples of the same class can be more different than those from different classes. This issue is mainly due to changes in deformation, pose, and the presence of clutter. In the literature, attention has been one of the most successful strategies to handle the aforementioned problems. Attention has been typically implemented in neural networks by selecting the most informative regions of the image that improve classification. In contrast, in this paper, attention is not applied at the image level but to the convolutional feature activations. In essence, with our approach, the neural model learns to attend to lower-level feature activations without requiring part annotations and uses those activations to update and rectify the output likelihood distribution. The proposed mechanism is modular, architecture-independent, and efficient in terms of both parameters and computation required. Experiments demonstrate that well-known networks such as wide residual networks and ResNeXt, when augmented with our approach, systematically improve their classification accuracy and become more robust to changes in deformation and pose and to the presence of clutter. As a result, our proposal reaches state-of-the-art classification accuracies in CIFAR-10, the Adience gender recognition task, Stanford Dogs, and UEC-Food100 while obtaining competitive performance in ImageNet, CIFAR-100, CUB200 Birds, and Stanford Cars. In addition, we analyze the different components of our model, showing that the proposed attention modules succeed in finding the most discriminative regions of the image. Finally, as a proof of concept, we demonstrate that with only local predictions, an augmented neural network can successfully classify an image before reaching any fully connected layer, thus reducing the computational amount up to 10%.
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Michael Holte, Bhaskar Chakraborty, Jordi Gonzalez, & Thomas B. Moeslund. (2012). A Local 3D Motion Descriptor for Multi-View Human Action Recognition from 4D Spatio-Temporal Interest Points. J-STSP - IEEE Journal of Selected Topics in Signal Processing, 6(5), 553–565.
Abstract: In this paper, we address the problem of human action recognition in reconstructed 3-D data acquired by multi-camera systems. We contribute to this field by introducing a novel 3-D action recognition approach based on detection of 4-D (3-D space $+$ time) spatio-temporal interest points (STIPs) and local description of 3-D motion features. STIPs are detected in multi-view images and extended to 4-D using 3-D reconstructions of the actors and pixel-to-vertex correspondences of the multi-camera setup. Local 3-D motion descriptors, histogram of optical 3-D flow (HOF3D), are extracted from estimated 3-D optical flow in the neighborhood of each 4-D STIP and made view-invariant. The local HOF3D descriptors are divided using 3-D spatial pyramids to capture and improve the discrimination between arm- and leg-based actions. Based on these pyramids of HOF3D descriptors we build a bag-of-words (BoW) vocabulary of human actions, which is compressed and classified using agglomerative information bottleneck (AIB) and support vector machines (SVMs), respectively. Experiments on the publicly available i3DPost and IXMAS datasets show promising state-of-the-art results and validate the performance and view-invariance of the approach.
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