Beschreibung:
Visual object tracking uses cameras to track target objects in the environment, which has many applications nowadays, such as intelligent surveillance, medical care, intelligent transportation and human-machine interaction. However, it is still a challenging task because of background noises, occlusions, illumination changes and fast motion. The goal of this dissertation is to improve measurements in Bayesian filtering frameworks for visual object tracking as follows: First, we combine multiple visual cues to improve the measurement for lane tracking. The lane is modeled by a linear-parabolic shape, which is a trade-off between accuracy of the fit and robustness with respect to image artifacts. In contrast to previous methods for linear-parabolic lane tracking, we use not only the color and edge information, but also the gradient orientation as visual cues. The lane tracking becomes a statistical reference problem when these local visual cues are available. The probabilistic distribution of lane parameters are estimated from the visual cues by multiple kernel density estimation, which is proved to be very robust to the image noise. Furthermore we use this probabilistic distribution function as the measurement model of the partitioned particle filter to update lane parameters. The experiments show that our novel lane tracking framework has its strength in a new combination and improvement of various advanced methods ...
Maschinelles Sehen; Bayes Filter; Objekt Tracking; Information Filter; Straßen Tracking; Sensorfusion; farbinvariante Histogramme; Quadratwurzel Filter; Square-Root Central Difference Information Filter