We propose a novel method for improving algorithms which detect the presence of people in video sequences. Our focus is on algorithms for applications which require reporting and analyzing all scenes with detected people in long recordings. Therefore one of the target qualities of the classification result is its stability, understood as a low number of invalid scene boundaries. Many existing methods process images in the recording...

We propose a novel method of improving algorithms recognizing traffic lights in video sequences. Our focus is on algorithms for applications which notify the driver of a light in sight. Many existing methods process images in the recording separately. Our method bases on the observation that real-life videos depict underlying continuous processes. We named our method FSA (Frame Sequence Analyzed). It is applicable for any underlying...

We consider binary classification algorithms, which operate on single frames from video sequences. Such a class of algorithms is named OFA (One Frame Analyzed). Two such algorithms for facial detection are compared in terms of their susceptibility to the FSA (Frame Sequence Analysis) method. It introduces a shifting time-window improvement, which includes the temporal context of frames in a post-processing step that improves the...

In the dissertation a new method for improving the quality of classifications of images in video streams has been proposed and analyzed. In multiple fields concerning such a classification, the proposed algorithms focus on the analysis of single frames. This class of algorithms has been named OFA (One Frame Analyzed).In the dissertation, small segments of the video are considered and each image is analyzed in the context of its...