Nataliya Shapovalova, Wenjuan Gong, Marco Pedersoli, Xavier Roca, & Jordi Gonzalez. (2011). On Importance of Interactions and Context in Human Action Recognition. In and M. Hernandez J. M. S. J. Vitria (Ed.), 5th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 6669, pp. 58–66). LNCS. Springer Berlin Heidelberg.
Abstract: This paper is focused on the automatic recognition of human events in static images. Popular techniques use knowledge of the human pose for inferring the action, and the most recent approaches tend to combine pose information with either knowledge of the scene or of the objects with which the human interacts. Our approach makes a step forward in this direction by combining the human pose with the scene in which the human is placed, together with the spatial relationships between humans and objects. Based on standard, simple descriptors like HOG and SIFT, recognition performance is enhanced when these three types of knowledge are taken into account. Results obtained in the PASCAL 2010 Action Recognition Dataset demonstrate that our technique reaches state-of-the-art results using simple descriptors and classifiers.
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Carme Julia, Angel Sappa, Felipe Lumbreras, Joan Serrat, & Antonio Lopez. (2007). Motion Segmentation from Feature Trajectories with Missing Data. In J. Marti et al.(Eds.) (Ed.), 3rd. Iberian Conference on Pattern Recognition and Image Analysis (Vol. LNCS 4477, 483–490).
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David Geronimo, Antonio Lopez, & Angel Sappa. (2007). Computer Vision Approaches for Pedestrian Detection: Visible Spectrum Survey. In J. Marti et al. (Ed.), 3rd Iberian Conference on Pattern Recognition and Image Analysis, LNCS 4477 (Vol. 1, 547–554).
Abstract: Pedestrian detection from images of the visible spectrum is a high relevant area of research given its potential impact in the design of pedestrian protection systems. There are many proposals in the literature but they lack a comparative viewpoint. According to this, in this paper we first propose a common framework where we fit the different approaches, and second we use this framework to provide a comparative point of view of the details of such different approaches, pointing out also the main challenges to be solved in the future. In summary, we expect
this survey to be useful for both novel and experienced researchers in the field. In the first case, as a clarifying snapshot of the state of the art; in the second, as a way to unveil trends and to take conclusions from the comparative study.
Keywords: Pedestrian detection
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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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Joan Serrat, Ferran Diego, Felipe Lumbreras, & Jose Manuel Alvarez. (2007). Synchronization of Video Sequences from Free-moving Cameras. In J. Marti et al. (Ed.), 3rd Iberian Conference on Pattern Recognition and Image Analysis (Vol. 4477, 620–627). LNCS.
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Agata Lapedriza, David Masip, & Jordi Vitria. (2007). A Hierarchical Approach for Multi-task Logistic Regression. In J. Marti et al. (Ed.), 3rd Iberian Conference on Pattern Recognition and Image Analysis (Vol. 4478, 258–265). LNCS.
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Antonio Lopez, Joan Serrat, Cristina Cañero, & Felipe Lumbreras. (2007). Robust Lane Lines Detection and Quantitative Assessment. In J. Marti et al (Ed.), 3rd Iberian Conference on Pattern Recognition and Image Analysis (Vol. 4477, 274–281). LNCS.
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Partha Pratim Roy, Eduard Vazquez, Josep Llados, Ramon Baldrich, & Umapada Pal. (2007). A System to Retrieve Text/Symbols from Color Maps using Connected Component and Skeleton Analysis. In J.M. Ogier W. L. J. Llados (Ed.), Seventh IAPR International Workshop on Graphics Recognition (79–78).
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Joan Mas, J.A. Jorge, Gemma Sanchez, & Josep Llados. (2007). Describing and Parising Hand-Drawn Sketches using a Syntactic Approach. In J.M. Ogier W. L. J. Llados (Ed.), Seventh IAPR International Workshop on Graphics Recognition (61–62).
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Marçal Rusiñol, & Josep Llados. (2007). A Region-Based Hashing Approach for Symbol Spotting in Thechnical Documents. In J.M. Ogier W. L. J. Llados (Ed.), Seventh IAPR International Workshop on Graphics Recognition (41–42).
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Josep Llados. (2006). Computer Vision: Progress of Research and Development ( J. Llados(ed.), Ed.).
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Fadi Dornaika, & Angel Sappa. (2007). Improving Appearance-Based 3D Face Tracking Using Sparse Stereo Data. In H. Araujo and J. Jorge A. R. J. Braz (Ed.), Advances in Computer Graphics and Computer Vision, (354–366). Springer Verlag.
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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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Yainuvis Socarras, David Vazquez, Antonio Lopez, David Geronimo, & Theo Gevers. (2012). Improving HOG with Image Segmentation: Application to Human Detection. In J. Blanc-Talon et al. (Ed.), 11th International Conference on Advanced Concepts for Intelligent Vision Systems (Vol. 7517, pp. 178–189). LNCS. Springer Berlin Heidelberg.
Abstract: In this paper we improve the histogram of oriented gradients (HOG), a core descriptor of state-of-the-art object detection, by the use of higher-level information coming from image segmentation. The idea is to re-weight the descriptor while computing it without increasing its size. The benefits of the proposal are two-fold: (i) to improve the performance of the detector by enriching the descriptor information and (ii) take advantage of the information of image segmentation, which in fact is likely to be used in other stages of the detection system such as candidate generation or refinement.
We test our technique in the INRIA person dataset, which was originally developed to test HOG, embedding it in a human detection system. The well-known segmentation method, mean-shift (from smaller to larger super-pixels), and different methods to re-weight the original descriptor (constant, region-luminance, color or texture-dependent) has been evaluated. We achieve performance improvements of 4:47% in detection rate through the use of differences of color between contour pixel neighborhoods as re-weighting function.
Keywords: Segmentation; Pedestrian Detection
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David Aldavert, Arnau Ramisa, Ramon Lopez de Mantaras, & Ricardo Toledo. (2010). Real-time Object Segmentation using a Bag of Features Approach. In J.Aguilar. A. M. In R.Alquezar (Ed.), 13th International Conference of the Catalan Association for Artificial Intelligence (Vol. 220, 321–329). IOS Press Amsterdam,.
Abstract: In this paper, we propose an object segmentation framework, based on the popular bag of features (BoF), which can process several images per second while achieving a good segmentation accuracy assigning an object category to every pixel of the image. We propose an efficient color descriptor to complement the information obtained by a typical gradient-based local descriptor. Results show that color proves to be a useful cue to increase the segmentation accuracy, specially in large homogeneous regions. Then, we extend the Hierarchical K-Means codebook using the recently proposed Vector of Locally Aggregated Descriptors method. Finally, we show that the BoF method can be easily parallelized since it is applied locally, thus the time necessary to process an image is further reduced. The performance of the proposed method is evaluated in the standard PASCAL 2007 Segmentation Challenge object segmentation dataset.
Keywords: Object Segmentation; Bag Of Features; Feature Quantization; Densely sampled descriptors
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