Semi-supervised Trajectory Learning Using a Multi-Scale Key Point Based Trajectory Representation

Motion trajectories contain rich high-level semantic information such as object behaviors and gestures, which can be effectively captured by supervised trajectory learning. However, it is usually a tough task to obtain a large number of high-quality manually labeled samples in real applications. Thus, how to perform trajectory learning in small training sample size situations is an important research topic. In this paper, we propose a trajectory learning framework using graph-based semi-supervised transductive learning, which propagates training sample labels along a particular graph. Furthermore, a novel trajectory descriptor based on multi-scale key points is proposed to characterize the spatial structural information. Experimental results demonstrate effectiveness of our framework.