Markov random field-based real-time detection of intentionally-captured persons
Tatsuya Koyama, Yuta Nakashima, Noboru Babaguchi · 2012
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.