Liu, Y.Li, X.Hu, W.2011-11-102011-11-102010Proceedings, 2010 20th International Conference on Pattern Recognition: ICPR 2010 / pp.3525-35289781424475421http://hdl.handle.net/2440/67309Motion 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.en© 2010 IEEEConference paper002011268210.1109/ICPR.2010.86027770