Knowledge Transfer For Scene-Specific Motion Prediction
COMPUTER VISION - ECCV 2016, PT I(2016)
摘要
When given a single frame of the video, humans can not only interpret the content of the scene, but also they are able to forecast the near future. This ability is mostly driven by their rich prior knowledge about the visual world, both in terms of (i) the dynamics of moving agents, as well as (ii) the semantic of the scene. In this work we exploit the interplay between these two key elements to predict scene-specific motion patterns. First, we extract patch descriptors encoding the probability of moving to the adjacent patches, and the probability of being in that particular patch or changing behavior. Then, we introduce a Dynamic Bayesian Network which exploits this scene specific knowledge for trajectory prediction. Experimental results demonstrate that our method is able to accurately predict trajectories and transfer predictions to a novel scene characterized by similar elements.
更多查看译文
关键词
Knowledge Transfer,Dynamic Bayesian Network,Semantic Context,Context Descriptor,Input Scene
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络