17,988 research outputs found
Automated Ground Truth Estimation For Automotive Radar Tracking Applications With Portable GNSS And IMU Devices
Baseline generation for tracking applications is a difficult task when
working with real world radar data. Data sparsity usually only allows an
indirect way of estimating the original tracks as most objects' centers are not
represented in the data. This article proposes an automated way of acquiring
reference trajectories by using a highly accurate hand-held global navigation
satellite system (GNSS). An embedded inertial measurement unit (IMU) is used
for estimating orientation and motion behavior. This article contains two major
contributions. A method for associating radar data to vulnerable road user
(VRU) tracks is described. It is evaluated how accurate the system performs
under different GNSS reception conditions and how carrying a reference system
alters radar measurements. Second, the system is used to track pedestrians and
cyclists over many measurement cycles in order to generate object centered
occupancy grid maps. The reference system allows to much more precisely
generate real world radar data distributions of VRUs than compared to
conventional methods. Hereby, an important step towards radar-based VRU
tracking is accomplished.Comment: 10 pages, 9 figures, accepted paper for 2019 20th International Radar
Symposium (IRS), Ulm, Germany, June 2019. arXiv admin note: text overlap with
arXiv:1905.1121
Joint 3D Proposal Generation and Object Detection from View Aggregation
We present AVOD, an Aggregate View Object Detection network for autonomous
driving scenarios. The proposed neural network architecture uses LIDAR point
clouds and RGB images to generate features that are shared by two subnetworks:
a region proposal network (RPN) and a second stage detector network. The
proposed RPN uses a novel architecture capable of performing multimodal feature
fusion on high resolution feature maps to generate reliable 3D object proposals
for multiple object classes in road scenes. Using these proposals, the second
stage detection network performs accurate oriented 3D bounding box regression
and category classification to predict the extents, orientation, and
classification of objects in 3D space. Our proposed architecture is shown to
produce state of the art results on the KITTI 3D object detection benchmark
while running in real time with a low memory footprint, making it a suitable
candidate for deployment on autonomous vehicles. Code is at:
https://github.com/kujason/avodComment: For any inquiries contact aharakeh(at)uwaterloo(dot)c
Is the Pedestrian going to Cross? Answering by 2D Pose Estimation
Our recent work suggests that, thanks to nowadays powerful CNNs, image-based
2D pose estimation is a promising cue for determining pedestrian intentions
such as crossing the road in the path of the ego-vehicle, stopping before
entering the road, and starting to walk or bending towards the road. This
statement is based on the results obtained on non-naturalistic sequences
(Daimler dataset), i.e. in sequences choreographed specifically for performing
the study. Fortunately, a new publicly available dataset (JAAD) has appeared
recently to allow developing methods for detecting pedestrian intentions in
naturalistic driving conditions; more specifically, for addressing the relevant
question is the pedestrian going to cross? Accordingly, in this paper we use
JAAD to assess the usefulness of 2D pose estimation for answering such a
question. We combine CNN-based pedestrian detection, tracking and pose
estimation to predict the crossing action from monocular images. Overall, the
proposed pipeline provides new state-of-the-art results.Comment: This is a paper presented in IEEE Intelligent Vehicles Symposium
(IEEE IV 2018
Pedestrian detection in uncontrolled environments using stereo and biometric information
A method for pedestrian detection from challenging real world outdoor scenes is presented in this paper. This technique is able to extract multiple pedestrians, of varying orientations and appearances, from a scene even when faced with large and multiple occlusions. The technique is also robust to changing background lighting conditions and effects, such as shadows. The technique applies an enhanced method from which reliable disparity information can be obtained even from untextured homogeneous areas within a scene. This is used in conjunction with ground plane estimation and biometric information,to obtain reliable pedestrian regions. These regions are robust to erroneous areas of disparity data and also to severe pedestrian occlusion, which often occurs in unconstrained scenarios
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