39 research outputs found
Context-Aware Trajectory Prediction
Human motion and behaviour in crowded spaces is influenced by several
factors, such as the dynamics of other moving agents in the scene, as well as
the static elements that might be perceived as points of attraction or
obstacles. In this work, we present a new model for human trajectory prediction
which is able to take advantage of both human-human and human-space
interactions. The future trajectory of humans, are generated by observing their
past positions and interactions with the surroundings. To this end, we propose
a "context-aware" recurrent neural network LSTM model, which can learn and
predict human motion in crowded spaces such as a sidewalk, a museum or a
shopping mall. We evaluate our model on a public pedestrian datasets, and we
contribute a new challenging dataset that collects videos of humans that
navigate in a (real) crowded space such as a big museum. Results show that our
approach can predict human trajectories better when compared to previous
state-of-the-art forecasting models.Comment: Submitted to BMVC 201
Physical Primitive Decomposition
Objects are made of parts, each with distinct geometry, physics,
functionality, and affordances. Developing such a distributed, physical,
interpretable representation of objects will facilitate intelligent agents to
better explore and interact with the world. In this paper, we study physical
primitive decomposition---understanding an object through its components, each
with physical and geometric attributes. As annotated data for object parts and
physics are rare, we propose a novel formulation that learns physical
primitives by explaining both an object's appearance and its behaviors in
physical events. Our model performs well on block towers and tools in both
synthetic and real scenarios; we also demonstrate that visual and physical
observations often provide complementary signals. We further present ablation
and behavioral studies to better understand our model and contrast it with
human performance.Comment: ECCV 2018. Project page: http://ppd.csail.mit.edu
Perceiving Physical Equation by Observing Visual Scenarios
Inferring universal laws of the environment is an important ability of human
intelligence as well as a symbol of general AI. In this paper, we take a step
toward this goal such that we introduce a new challenging problem of inferring
invariant physical equation from visual scenarios. For instance, teaching a
machine to automatically derive the gravitational acceleration formula by
watching a free-falling object. To tackle this challenge, we present a novel
pipeline comprised of an Observer Engine and a Physicist Engine by respectively
imitating the actions of an observer and a physicist in the real world.
Generally, the Observer Engine watches the visual scenarios and then extracting
the physical properties of objects. The Physicist Engine analyses these data
and then summarizing the inherent laws of object dynamics. Specifically, the
learned laws are expressed by mathematical equations such that they are more
interpretable than the results given by common probabilistic models.
Experiments on synthetic videos have shown that our pipeline is able to
discover physical equations on various physical worlds with different visual
appearances.Comment: NIPS 2018 Workshop on Modeling the Physical Worl