Аннотация:This work considers the likelihood models based on similarity measures extracted from image
features which are widely used in the field of video tracking using particle filters. New computationally
optimal methods for multiple feature extraction from several regions of the same
image are proposed. The optimization is performed by using integral images, first prominently
used in computer vision within Viola–Jones object detection framework for Haar rectangles
and for other studied features. It is experimentally demonstrated that feature compositions
can be used even in the tasks where each of them is useless by itself. The performance achieved
using the proposed compositions is greater than one in the similar study and comparable to
the performance of more complicated models based on ensemble boosting.