We present a novel analysis of the state of the art in object tracking with respect to diversity found in its main component, an ensemble classifier that is updated in an online manner. We employ established measures for diversity and performance from the rich literature on ensemble classification and online learning, and present a detailed evaluation of diversity and performance on benchmark sequences in order to gain an insight into how the tracking performance can be improved. © Springer-Verlag 2013.
|Lecture Notes in Computer Science
|Image Processing, Computer Vision, Pattern Recognition, and Graphics
|11th International Workshop on Multiple Classifier Systems
|15/05/13 → 17/05/13