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Technologies of Vision: dynamic scene understanding

adaptive learning

Alpha Tracking Multiple People with Illumination Maps

Realtime multi-object tracking in an illumination-varying environment is a classical task in computer vision. Many approaches have been proposed in the literature but the problem is still far from being solved. In order to obtain robust tracking results, some methods simply discard the illumination-sensitive color information and employ other features that are considered invariant to illumination such as edges or textures. However, the main problem with such approaches is that in case of cluttered background, edges or textures are often not sufficient for reliably differentiating moving object contours from their background. Other approaches still rely on color information. However in order to handle illumination changes a common strategy is to adopt a color space different from RGB such as YUV or HSI in order to eliminate the intensity component. The shortcoming is that the feature discrimination capability is reduced, since only parts of the color channels are used. In the domain of tracking with particle filters, a scarcely investigated but promising method for dealing with varying illuminations conditions consists in a unified approach for jointly estimating the positions of the targets and their illumination conditions. The motivation behind this is that target localization strongly depends on object appearance and at the same time illumination conditions of a target are influenced by its position in the scene. Starting from this idea, in this project, we aim to develop a new algorithm for visual tracking of multiple people under non-homogenous and time-varying illumination conditions.

Learning People Trajectories Learning Pedestrian Trajectories

The automatic analysis of usual patterns is crucial for many video surveillance applications such as visual object tracking or anomaly detection. A typical example is represented by pedestrian trajectories. Usually, pedestrians tend to follow only a few common trajectories whilst other paths are very infrequent or never observed. In this project we aim to investigate novel approches for learning pedestrian trajectories.