Tesis doctoral de Carlos Roberto Del Blanco Adán
The increasing availability of powerful computers and high quality video cameras has allowed the proliferation of video based systems, which perform tasks such as vehicle navigation, traffic monitoring, surveillance, etc. A fundamental component in these systems is the visual tracking of objects of interest, whose main goal is to estimate the object trajectories in a video sequence. For this purpose, two different kinds of information are used: detections obtained by the analysis of video streams and prior knowledge about the object dynamics. However, this information is usually corrupted by the sensor noise, the varying object appearance, illumination changes, cluttered backgrounds, object interactions, and the camera ego-motion. while there exist reliable algorithms for tracking a single object in constrained scenarios, the object tracking is still a challenge in uncontrolled situations involving multiple interacting objects, heavily-cluttered scenarios, moving cameras, and complex object dynamics. In this dissertation, the aim has been to develop efficient tracking solutions for two complex tracking situations. The first one consists in tracking a single object in heavily-cluttered scenarios with a moving camera. To address this situation, an advanced bayesian framework has been designed that jointly models the object and camera dynamics. As a result, it can predict satisfactorily the evolution of a tracked object in situations with high uncertainty about the object location. In addition, the algorithm is robust to the background clutter, avoiding tracking failures due to the presence of similar objects. the other tracking situation focuses on the interactions of multiple objects with a static camera. To tackle this problem, a novel bayesian model has been developed, which manages complex object interactions by means of an advanced object dynamic model that is sensitive to object interactions. This is achieved by inferring the occlusion events, which in turn trigger different choices of object motion. The tracking algorithm can also handle false and missing detections through a probabilistic data association stage. excellent results have been obtained using publicly available databases, proving the efficiency of the developed bayesian tracking models.
Datos académicos de la tesis doctoral «Visual object tracking in challenging situations using a bayesian perspective«
- Título de la tesis: Visual object tracking in challenging situations using a bayesian perspective
- Autor: Carlos Roberto Del Blanco Adán
- Universidad: Politécnica de Madrid
- Fecha de lectura de la tesis: 04/02/2011
Dirección y tribunal
- Director de la tesis
- Fernando Jaureguizar Núñez
- Tribunal
- Presidente del tribunal: narciso García santos
- montserrat Pardas feliu (vocal)
- joaquin Miguez arenas (vocal)
- Andrea Cavallaro (vocal)