## 2 Related Work  
To date, a widespread approach for detecting pedestrians is the single use of grey scale [9] and color information [10; 11; 12]. In the approach in [13], the scene background is dynamically adapted by filtering elements, such as shadows, specular reflections, _etc._, that appear after usual background subtraction algorithms. Another proposal uses histograms of oriented gradients to perform an initial step in human detection [14]. These detections are classified by means of support vector machines (SVMs). The most interesting contribution is the description of opponent color space as a biologically-inspired alternative to detect humans. The authors demonstrate that their method achieves superior results compared to the equivalent segmentation using the RGB color space. However, using visible-light information is usually problematic when facing changes in lighting in a scene or when there are illumination problems in a scene, such as fog or zones covered by darkness. Images in the thermal infrared spectrum show a set of differentiating features compared with visible spectrum camera frames [15; 16; 17]. The grey level value of objects in infrared stems from their temperature and radiated heat and does not depend on the illumination. When adopting a people detection algorithm in the thermal infrared spectrum, heat is taken into account, as people appear warmer than other elements in the scenario. Nonetheless, this is not always true [5]. This is usually faithfully fulfilled in winter and at night. In addition, due to the technological limitations of infrared cameras, many infrared images have low spatial resolution and lower sensitivity than images of the visible spectrum. The limitations usually result in a large number of image noise and low image quality. Many approaches combine the properties of appearance and shape in this spectrum. Indeed, humans are initially segmented from their appearance (they generally look brighter than other objects in the scene) and filtered and sorted on a shape basis. For instance, in [16], human candidates are initially detected by a thresholding procedure, using the previously described idea. The candidates found are decomposed into a series of layers using wavelets. Their features are then extracted through the use of high-frequency bands. Finally, human regions are classified by an SVM. Even so, there remains some problems to solve at the time of performing an accurate segmentation of humans, such as, for instance, the appearance of halos that decrease accuracy when defining the outlines of the silhouettes or problems that arise in situations where the temperature of objects and persons present in the scene are quite homogeneous. In [18], these problems are addressed using a histogram-based segmentation, where vertical and horizontal adjustments are realized in human candidate regions. Two different alternatives are established for winter and summer conditions. While grey value levels are used in the former to delimit humans, intensity changes in the human limits are searched for in the latter. In order to take advantage of the strengths of both visible-light and thermal infrared sensors, some image fusion techniques are arising. In this sense, a background-subtraction technique that fuses contours from thermal and visible imagery for persistent object detection in urban settings is presented [19]. Statistical background subtraction in the thermal domain is used to identify the initial regions-of-interest. Color and intensity information are used within these areas to obtain the corresponding regions-of-interest in the visible domain. The objective of another work is to authenticate individuals based on the appearance of their faces [20]. The main novelty is an algorithm for the decision-level fusion of two types of imagery: one acquired in the visual and one acquired in the infrared electromagnetic spectrum. Very recently, a new method based on a nonsubsampled contourlet transform, has been proposed to fuse the infrared image and the visible light image [21].