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Quantitative Precipitation Nowcasting: A Lagrangian Pixel-Based Approach
Short-term high-resolution precipitation forecasting has important implications for navigation, flood forecasting, and other hydrological and meteorological concerns. This article introduces a pixel-based algorithm for Short-term Quantitative Precipitation Forecasting (SQPF) using radar-based rainfall data. The proposed algorithm called Pixel- Based Nowcasting (PBN) tracks severe storms with a hierarchical mesh-tracking algorithm to capture storm advection in space and time at high resolution from radar imagers. The extracted advection field is then extended to nowcast the rainfall field in the next 3. hr based on a pixel-based Lagrangian dynamic model. The proposed algorithm is compared with two other nowcasting algorithms (WCN: Watershed-Clustering Nowcasting and PER: PERsistency) for ten thunderstorm events over the conterminous United States. Object-based verification metric and traditional statistics have been used to evaluate the performance of the proposed algorithm. It is shown that the proposed algorithm is superior over comparison algorithms and is effective in tracking and predicting severe storm events for the next few hours. © 2012 Elsevier B.V
Extended Object Tracking: Introduction, Overview and Applications
This article provides an elaborate overview of current research in extended
object tracking. We provide a clear definition of the extended object tracking
problem and discuss its delimitation to other types of object tracking. Next,
different aspects of extended object modelling are extensively discussed.
Subsequently, we give a tutorial introduction to two basic and well used
extended object tracking approaches - the random matrix approach and the Kalman
filter-based approach for star-convex shapes. The next part treats the tracking
of multiple extended objects and elaborates how the large number of feasible
association hypotheses can be tackled using both Random Finite Set (RFS) and
Non-RFS multi-object trackers. The article concludes with a summary of current
applications, where four example applications involving camera, X-band radar,
light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are
highlighted.Comment: 30 pages, 19 figure
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
People tracking by cooperative fusion of RADAR and camera sensors
Accurate 3D tracking of objects from monocular camera poses challenges due to the loss of depth during projection. Although ranging by RADAR has proven effective in highway environments, people tracking remains beyond the capability of single sensor systems. In this paper, we propose a cooperative RADAR-camera fusion method for people tracking on the ground plane. Using average person height, joint detection likelihood is calculated by back-projecting detections from the camera onto the RADAR Range-Azimuth data. Peaks in the joint likelihood, representing candidate targets, are fed into a Particle Filter tracker. Depending on the association outcome, particles are updated using the associated detections (Tracking by Detection), or by sampling the raw likelihood itself (Tracking Before Detection). Utilizing the raw likelihood data has the advantage that lost targets are continuously tracked even if the camera or RADAR signal is below the detection threshold. We show that in single target, uncluttered environments, the proposed method entirely outperforms camera-only tracking. Experiments in a real-world urban environment also confirm that the cooperative fusion tracker produces significantly better estimates, even in difficult and ambiguous situations
An Intelligent Monitoring System of Vehicles on Highway Traffic
Vehicle speed monitoring and management of highways is the critical problem
of the road in this modern age of growing technology and population. A poor
management results in frequent traffic jam, traffic rules violation and fatal
road accidents. Using traditional techniques of RADAR, LIDAR and LASAR to
address this problem is time-consuming, expensive and tedious. This paper
presents an efficient framework to produce a simple, cost efficient and
intelligent system for vehicle speed monitoring. The proposed method uses an HD
(High Definition) camera mounted on the road side either on a pole or on a
traffic signal for recording video frames. On the basis of these frames, a
vehicle can be tracked by using radius growing method, and its speed can be
calculated by calculating vehicle mask and its displacement in consecutive
frames. The method uses pattern recognition, digital image processing and
mathematical techniques for vehicle detection, tracking and speed calculation.
The validity of the proposed model is proved by testing it on different
highways.Comment: 5 page
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