This thesis tackles the problem of fast object detection based on template models. Searching for an object in an image is the procedure of evaluating the similarity between the template model and every possible image location and scale. Here we argue that using a template model representation based on a multiple resolution hierarchy is an optimal choice that can lead to excellent detection accuracy and fast computation. As the search of the object is implicitly efectuated at multiple image resolutions to detect objects at multiple scales, using also a template model with multiple resolutions permits an improved model representation almost without any additional computational cost.

Nowadays the Bag-of-Words (BoW) based image representation is the most successful approach in the context of object and scene classification tasks. However, its main drawback is the absence of the important spatial information. Spatial pyramids (SP) have been successfully applied to incorporate spatial information into BoW-based image representation. Within the SP framework, the optimal way for obtaining an image spatial representation which is able to cope with it’s most foremost shortcomings, concretely, it’s high dimensionality and the rigidity of the resulting image representation still remains an active research domain. In summary, the main concern of this thesis is to search for the limits of spatial pyramids and try to figure out solutions for them. This thesis explores the problem of obtaining compact, adaptive, yet informative spatial image representations in the context of object and scene classification tasks.

This thesis is devoted to moving shadows detection and suppression. Shadows could be defined as the parts of the scene that are not directly illuminated by a light source due to obstructing object or objects. Often, moving shadows in images sequences are undesirable since they could cause degradation of the expected results during processing of images for object detection, segmentation, scene surveillance or similar purposes. In this thesis first moving shadow detection methods are exhaustively overviewed. Beside the mentioned methods from literature and to compensate their limitations, a new moving shadow detection method is proposed.

Model-based tracking approaches, and in particular particle filters, formulate the problem as a Bayesian inference task whose aim is to sequentially estimate the distribution of the parameters of a human body model over time. These approaches strongly rely on good dynamical and observation models to predict and update congurations of the human body according to measurements from the image data. However, it is very difficult to design observation models which extract useful and reliable information from image sequences robustly. Therefore, to overcome these limitations strong motion priors are considered in this Thesis to guide the exploration of the state space.

This Thesis is mainly divided in two parts. The rst one presents a study of motion segmentation problems. Based on this study, a novel algorithm for mobile-object segmentation from a static background scene is also presented. This approach is demonstrated robust and accurate under most of the common problems in motion segmentation. The second one tackles the problem of shadows in depth. Firstly a bottom-up approach based on a chromatic shadow detector is presented to deal with umbra shadows. Secondly, a top-down approach based on a tracking system has been developed in order to enhance the chromatic shadow detection.

The increasing ubiquitousness of digital information in our daily lives has positioned video as a favored information vehicle, and given rise to an astonishing generation of social media and surveillance footage. This raises a series of technological demands for automatic video understanding and management, which together with the compromising attentional limitations of human operators, have motivated the research community to guide its steps towards a better attainment of such capabilities. In this thesis we tackle the problem of recognizing and describing meaningful events in video sequences from different domains, and communicating the resulting knowledge to end-users by means of advanced interfaces for human–computer interaction.

Psychological evidence has emphasized the importance of affective behaviour understanding due to its high impact in nowadays interaction humans and computers. All type of affective and behavioural patterns such as gestures, emotions and mental states are highly displayed through the face, head and body. Therefore, this thesis is focused to analyse affective behaviours on head and face. To this end, head and facial movements are encoded by using appearance based tracking methods. Specifically, a wise combination of deformable models captures rigid and non-rigid movements of different kinematics; 3D head pose, eyebrows, mouth, eyelids and irises are taken into account as basis for extracting features from databases of video sequences.

This Thesis tackles the problem of obtaining a proper representation of human behavior in the contexts of computer vision and animation. On the one hand, a good behavior model should permit the recognition and explanation the observed activity in image sequences. On the other hand, such a model must allow the generation of new synthetic instances, which model the behavior of virtual agents. Finally, we demonstrate the suitability of the proposed framework to simulate behavior of virtual agents, which are introduced into real image sequences and interact with observed real agents, thereby easing the generation of augmented reality sequences.

Natural Vision Systems have reached incredible performances in detecting and tracking multiple moving objects simultaneously. Accurate and robust multiple-target tracking is also a key task in many promising Computer-Vision applications. In this thesis, a principled hierarchical architecture which fulfils multiple-target tracking is presented. Thus, a modular and hierarchically-organised system is designed. It is conformed by a detection level which feeds a two-level tracking subsystem. Co-operating modules, distributed through this architecture, work following both bottom-up and top-down approaches. Contributions include both the architecture itself, and the development, improvement and integration of the different modules. The proposed architecture introduces the necessary synergies which allow the system to tackle such a problem as unconstrained multiple-target tracking.