Markov random field-based real-time detection of intentionally-captured persons

Most videos taken by videographers contain intentionally-captured persons (ICPs), who are essential for what the videographers want to express in their video. This paper presents a method to detect ICPs in real-time. Whether a person in a video is an ICP or not is reflected in features such as the person´s motion and camera motion, which are thus beneficial for detecting ICPs. However, estimating camera motion is computationally expensive. For real-time detection, we use samples of acceleration and angular velocity obtained from inertial sensors instead of estimating camera motion. Considering that pairwise constraints based on differences between persons´ sizes also improve the detection performance, we model the ICPs using Markov random field. We experimentally evaluate the performance of our method and demonstrate that it works in real-time.