64 research outputs found
Occlusion-Aware Object Localization, Segmentation and Pose Estimation
We present a learning approach for localization and segmentation of objects
in an image in a manner that is robust to partial occlusion. Our algorithm
produces a bounding box around the full extent of the object and labels pixels
in the interior that belong to the object. Like existing segmentation aware
detection approaches, we learn an appearance model of the object and consider
regions that do not fit this model as potential occlusions. However, in
addition to the established use of pairwise potentials for encouraging local
consistency, we use higher order potentials which capture information at the
level of im- age segments. We also propose an efficient loss function that
targets both localization and segmentation performance. Our algorithm achieves
13.52% segmentation error and 0.81 area under the false-positive per image vs.
recall curve on average over the challenging CMU Kitchen Occlusion Dataset.
This is a 42.44% decrease in segmentation error and a 16.13% increase in
localization performance compared to the state-of-the-art. Finally, we show
that the visibility labelling produced by our algorithm can make full 3D pose
estimation from a single image robust to occlusion.Comment: British Machine Vision Conference 2015 (poster
Deep Predictive Models for Collision Risk Assessment in Autonomous Driving
In this paper, we investigate a predictive approach for collision risk
assessment in autonomous and assisted driving. A deep predictive model is
trained to anticipate imminent accidents from traditional video streams. In
particular, the model learns to identify cues in RGB images that are predictive
of hazardous upcoming situations. In contrast to previous work, our approach
incorporates (a) temporal information during decision making, (b) multi-modal
information about the environment, as well as the proprioceptive state and
steering actions of the controlled vehicle, and (c) information about the
uncertainty inherent to the task. To this end, we discuss Deep Predictive
Models and present an implementation using a Bayesian Convolutional LSTM.
Experiments in a simple simulation environment show that the approach can learn
to predict impending accidents with reasonable accuracy, especially when
multiple cameras are used as input sources.Comment: 8 pages, 4 figure
DeepCrashTest: Turning Dashcam Videos into Virtual Crash Tests for Automated Driving Systems
The goal of this paper is to generate simulations with real-world collision
scenarios for training and testing autonomous vehicles. We use numerous dashcam
crash videos uploaded on the internet to extract valuable collision data and
recreate the crash scenarios in a simulator. We tackle the problem of
extracting 3D vehicle trajectories from videos recorded by an unknown and
uncalibrated monocular camera source using a modular approach. A working
architecture and demonstration videos along with the open-source implementation
are provided with the paper.Comment: 8 pages, 5 figures, ICRA 2020, Trajectory Extraction, Trajectory
Simulatio
- …