1,678 research outputs found
Survey on Vision-based Path Prediction
Path prediction is a fundamental task for estimating how pedestrians or
vehicles are going to move in a scene. Because path prediction as a task of
computer vision uses video as input, various information used for prediction,
such as the environment surrounding the target and the internal state of the
target, need to be estimated from the video in addition to predicting paths.
Many prediction approaches that include understanding the environment and the
internal state have been proposed. In this survey, we systematically summarize
methods of path prediction that take video as input and and extract features
from the video. Moreover, we introduce datasets used to evaluate path
prediction methods quantitatively.Comment: DAPI 201
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
Learning Structured Representations of Spatial and Interactive Dynamics for Trajectory Prediction in Crowded Scenes
Context plays a significant role in the generation of motion for dynamic
agents in interactive environments. This work proposes a modular method that
utilises a learned model of the environment for motion prediction. This
modularity explicitly allows for unsupervised adaptation of trajectory
prediction models to unseen environments and new tasks by relying on unlabelled
image data only. We model both the spatial and dynamic aspects of a given
environment alongside the per agent motions. This results in more informed
motion prediction and allows for performance comparable to the
state-of-the-art. We highlight the model's prediction capability using a
benchmark pedestrian prediction problem and a robot manipulation task and show
that we can transfer the predictor across these tasks in a completely
unsupervised way. The proposed approach allows for robust and label efficient
forward modelling, and relaxes the need for full model re-training in new
environments
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