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David Guillamet, & Jordi Vitria. (2002). Classifying Faces with Non-negative Matrix Factorization..
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David Guillamet, & Jordi Vitria. (2003). An Experimental Evaluation of K-nn for Linear Transforms of Positive Data. In In Pattern Recognition and Image Analysis, Lecture Notes in Computer Science. 2652:317–325.
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David Guillamet, & Jordi Vitria. (2003). Evaluation of distance metrics for recognition based on non-negative matrix factorization. PRL - Pattern Recognition Letters, 24(9-10), 1599 –1605.
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David Guillamet, B. Shiele, & Jordi Vitria. (2002). Analyzing Non-negative Matrix Factorization for Image Classification..
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David Guillamet, B. Moghaddam, & Jordi Vitria. (2003). Modeling High-Order Dependencies in Local Appearance Models.
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David Guillamet, B. Moghaddam, & Jordi Vitria. (2003). Higher-Order Dependencies in Local Appearance Models.
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David Guillamet, & B. Moghaddam. (2002). Joint Distribution of Local Image Features for Appearance Moldeling..
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David Guillamet. (1999). Reconeixement d´objectes en entorns poc controlats mitjançant metodes estadistics.
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David Guillamet. (2001). Color histogram classification using NMF.
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David Guillamet. (2004). Statistical Local Appearance Models for Object Recognition (Jordi Vitria, Ed.). Ph.D. thesis, , .
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David Geronimo, Joan Serrat, Antonio Lopez, & Ramon Baldrich. (2013). Traffic sign recognition for computer vision project-based learning. T-EDUC - IEEE Transactions on Education, 56(3), 364–371.
Abstract: This paper presents a graduate course project on computer vision. The aim of the project is to detect and recognize traffic signs in video sequences recorded by an on-board vehicle camera. This is a demanding problem, given that traffic sign recognition is one of the most challenging problems for driving assistance systems. Equally, it is motivating for the students given that it is a real-life problem. Furthermore, it gives them the opportunity to appreciate the difficulty of real-world vision problems and to assess the extent to which this problem can be solved by modern computer vision and pattern classification techniques taught in the classroom. The learning objectives of the course are introduced, as are the constraints imposed on its design, such as the diversity of students' background and the amount of time they and their instructors dedicate to the course. The paper also describes the course contents, schedule, and how the project-based learning approach is applied. The outcomes of the course are discussed, including both the students' marks and their personal feedback.
Keywords: traffic signs
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David Geronimo, Frederic Lerasle, & Antonio Lopez. (2012). State-driven particle filter for multi-person tracking. In J. Blanc-Talon et al. (Ed.), 11th International Conference on Advanced Concepts for Intelligent Vision Systems (Vol. 7517, pp. 467–478). Heidelberg: Springer.
Abstract: Multi-person tracking can be exploited in applications such as driver assistance, surveillance, multimedia and human-robot interaction. With the help of human detectors, particle filters offer a robust method able to filter noisy detections and provide temporal coherence. However, some traditional problems such as occlusions with other targets or the scene, temporal drifting or even the lost targets detection are rarely considered, making the systems performance decrease. Some authors propose to overcome these problems using heuristics not explained
and formalized in the papers, for instance by defining exceptions to the model updating depending on tracks overlapping. In this paper we propose to formalize these events by the use of a state-graph, defining the current state of the track (e.g., potential , tracked, occluded or lost) and the transitions between states in an explicit way. This approach has the advantage of linking track actions such as the online underlying models updating, which gives flexibility to the system. It provides an explicit representation to adapt the multiple parallel trackers depending on the context, i.e., each track can make use of a specific filtering strategy, dynamic model, number of particles, etc. depending on its state. We implement this technique in a single-camera multi-person tracker and test
it in public video sequences.
Keywords: human tracking
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David Geronimo, David Vazquez, & Arturo de la Escalera. (2017). Vision-Based Advanced Driver Assistance Systems. In Computer Vision in Vehicle Technology: Land, Sea, and Air.
Keywords: ADAS; Autonomous Driving
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David Geronimo, Antonio Lopez, Daniel Ponsa, & Angel Sappa. (2007). Haar Wavelets and Edge Orientation Histograms for On-Board Pedestrian Detection. In J. Marti et al. (Ed.), 3rd Iberian Conference on Pattern Recognition and Image Analysis, LNCS 4477 (Vol. 1, 418–425).
Keywords: Pedestrian detection
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David Geronimo, Antonio Lopez, Angel Sappa, & Thorsten Graf. (2010). Survey on Pedestrian Detection for Advanced Driver Assistance Systems. TPAMI - IEEE Transaction on Pattern Analysis and Machine Intelligence, 32(7), 1239–1258.
Abstract: Advanced driver assistance systems (ADASs), and particularly pedestrian protection systems (PPSs), have become an active research area aimed at improving traffic safety. The major challenge of PPSs is the development of reliable on-board pedestrian detection systems. Due to the varying appearance of pedestrians (e.g., different clothes, changing size, aspect ratio, and dynamic shape) and the unstructured environment, it is very difficult to cope with the demanded robustness of this kind of system. Two problems arising in this research area are the lack of public benchmarks and the difficulty in reproducing many of the proposed methods, which makes it difficult to compare the approaches. As a result, surveying the literature by enumerating the proposals one-after-another is not the most useful way to provide a comparative point of view. Accordingly, we present a more convenient strategy to survey the different approaches. We divide the problem of detecting pedestrians from images into different processing steps, each with attached responsibilities. Then, the different proposed methods are analyzed and classified with respect to each processing stage, favoring a comparative viewpoint. Finally, discussion of the important topics is presented, putting special emphasis on the future needs and challenges.
Keywords: ADAS, pedestrian detection, on-board vision, survey
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