Video Segmentation by Event Detection: A Novel One-class Classification Approach

Segmenting videos into meaningful image sequences of some particular activities is an interesting problem in computer vision. In this paper, a novel algorithm is presented to achieve this semantic video segmentation. The goal is to make the system work unsupervised and generic in terms of application scenarios. The segmentation task is accomplished through event detection in a frameby-frame processing setup. For event detection, we use a one-class classification approach based on Gaussian processes, which has been proved to be successful in object classification. The algorithm is tested on videos from a publicly available change detection database and the results clearly show the suitability of our approach for the task of video segmentation.