Systematic approach to binary classification of images in video streams using shifting time windows

Abstract

Multiple algorithms classifying frames in video sequences consider them only as separate images. After pointing out the properties of real-life recordings and classifications of their frames, we propose a new shifting time window approach for improving binary classifications. It proceeds in two steps: First, well-known classification algorithms are used separately for each frame to acquire preliminary classifications. Secondly, the results of the previous step are analyzed in relatively short sequences of consecutive images (the shifting time window). Taking into account the continuous nature of analyzed real-life videos, the preliminary binary classification sequences can be corrected. In consequence, the classification quality is improved. Furthermore, we offer a systematic approach where all parameters of the proposed algorithm (such as the window length or vote weight distribution in the window) are considered and their optimal values are determined. Experiments on representative examples confirm the advantages of the proposed approach.