27 research outputs found
Anticipating Visual Representations from Unlabeled Video
Anticipating actions and objects before they start or appear is a difficult
problem in computer vision with several real-world applications. This task is
challenging partly because it requires leveraging extensive knowledge of the
world that is difficult to write down. We believe that a promising resource for
efficiently learning this knowledge is through readily available unlabeled
video. We present a framework that capitalizes on temporal structure in
unlabeled video to learn to anticipate human actions and objects. The key idea
behind our approach is that we can train deep networks to predict the visual
representation of images in the future. Visual representations are a promising
prediction target because they encode images at a higher semantic level than
pixels yet are automatic to compute. We then apply recognition algorithms on
our predicted representation to anticipate objects and actions. We
experimentally validate this idea on two datasets, anticipating actions one
second in the future and objects five seconds in the future.Comment: CVPR 201
CAR-Net: Clairvoyant Attentive Recurrent Network
We present an interpretable framework for path prediction that leverages
dependencies between agents' behaviors and their spatial navigation
environment. We exploit two sources of information: the past motion trajectory
of the agent of interest and a wide top-view image of the navigation scene. We
propose a Clairvoyant Attentive Recurrent Network (CAR-Net) that learns where
to look in a large image of the scene when solving the path prediction task.
Our method can attend to any area, or combination of areas, within the raw
image (e.g., road intersections) when predicting the trajectory of the agent.
This allows us to visualize fine-grained semantic elements of navigation scenes
that influence the prediction of trajectories. To study the impact of space on
agents' trajectories, we build a new dataset made of top-view images of
hundreds of scenes (Formula One racing tracks) where agents' behaviors are
heavily influenced by known areas in the images (e.g., upcoming turns). CAR-Net
successfully attends to these salient regions. Additionally, CAR-Net reaches
state-of-the-art accuracy on the standard trajectory forecasting benchmark,
Stanford Drone Dataset (SDD). Finally, we show CAR-Net's ability to generalize
to unseen scenes.Comment: The 2nd and 3rd authors contributed equall
Knowledge transfer for scene-specific motion prediction
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