45,667 research outputs found
Multiple path prediction for traffic scenes using LSTMs and mixture density models
This work presents an analysis of predicting multiple future paths of moving objects in traffic scenes by leveraging Long Short-Term Memory architectures (LSTMs) and Mixture Density Networks (MDNs) in a single-shot manner. Path prediction allows estimating the future positions of objects. This is useful in important applications such as security monitoring systems, Autonomous Driver Assistance Systems and assistive technologies. Normal approaches use observed positions (tracklets) of objects in video frames to predict their future paths as a sequence of position values. This can be treated as a time series. LSTMs have achieved good performance when dealing with time series. However, LSTMs have the limitation of only predicting a single path per tracklet. Path prediction is not a deterministic task and requires predicting with a level of uncertainty. Predicting multiple paths instead of a single one is therefore a more realistic manner of approaching this task. In this work, predicting a set of future paths with associated uncertainty was archived by combining LSTMs and MDNs. The evaluation was made on the KITTI and the CityFlow datasets on three type of objects, four prediction horizons and two different points of view (image coordinates and birds-eye vie
Identifying Vessel Branching from Fluid Stresses on Microscopic Robots
Objects moving in fluids experience patterns of stress on their surfaces
determined by the geometry of nearby boundaries. Flows at low Reynolds number,
as occur in microscopic vessels such as capillaries in biological tissues, have
relatively simple relations between stresses and nearby vessel geometry. Using
these relations, this paper shows how a microscopic robot moving with such
flows can use changes in stress on its surface to identify when it encounters
vessel branches.Comment: Version 2 has minor clarification
FieldSAFE: Dataset for Obstacle Detection in Agriculture
In this paper, we present a novel multi-modal dataset for obstacle detection
in agriculture. The dataset comprises approximately 2 hours of raw sensor data
from a tractor-mounted sensor system in a grass mowing scenario in Denmark,
October 2016. Sensing modalities include stereo camera, thermal camera, web
camera, 360-degree camera, lidar, and radar, while precise localization is
available from fused IMU and GNSS. Both static and moving obstacles are present
including humans, mannequin dolls, rocks, barrels, buildings, vehicles, and
vegetation. All obstacles have ground truth object labels and geographic
coordinates.Comment: Submitted to special issue of MDPI Sensors: Sensors in Agricultur
Non-line-of-sight tracking of people at long range
A remote-sensing system that can determine the position of hidden objects has
applications in many critical real-life scenarios, such as search and rescue
missions and safe autonomous driving. Previous work has shown the ability to
range and image objects hidden from the direct line of sight, employing
advanced optical imaging technologies aimed at small objects at short range. In
this work we demonstrate a long-range tracking system based on single laser
illumination and single-pixel single-photon detection. This enables us to track
one or more people hidden from view at a stand-off distance of over 50~m. These
results pave the way towards next generation LiDAR systems that will
reconstruct not only the direct-view scene but also the main elements hidden
behind walls or corners
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