Semi-supervised Early Event Detection

Abstract

Early event detection is among the keys in the field of event detection due to its timeliness in widespread applications. The objective of early detection (ED) is to identify the specified event of the video sequence as early as possible before its ending. This paper introduces semi-supervised learning to ED, which is the first attempt to utilize the domain knowledge in the field of ED. In this setting, some domain knowledge in the form of pairwise constraints is available. Particularly, we treat the segments of complete events as must-link constraints. Furthermore, some segments do not overlap with the event are put together with the complete events as cannot-link constraints. Thus, a new algorithm termed semi-supervised ED (SemiED) is proposed, which could make better early detection for videos. The SemiED algorithm is a convex quadratic programming problem, which could be resolved efficiently. We also discuss the computational complexity of SemiED to evaluate its effectiveness. The superiority of the proposed method is validated on two video-based datasets.