Object detection in video with graphical models

Abstract

In this paper, we propose a general object detection frame-work which combines the Hidden Markov Model with the Discriminative Random Fields. Recent object detection algorithms have achieved impressive results by using graphical models, such as Markov Random Field. These models, however, have only been applied to two dimensional images. In many scenarios, video is the directly available source rather than images, hence an important information for detecting objects has been omitted - the temporal information. To demonstrate the importance of temporal information, we apply graphical models to the task of text detection in video and compare the result of with and without temporal information. We also show the superiority of the proposed models over simple heuristics such as median filter over time.