An official website of the United States government
In recent years, satellites capable of capturing videos have been developed and launched to provide high definition satellite videos that enable applications far beyond the capabilities of remotely sensed imagery. Moving object detection and moving object tracking are among the most essential and challenging tasks, but existing studies have mainly focused on vehicles. To accurately detect and then track more complex moving objects, specifically airplanes, we need to address the challenges posed by the new data. First, slow-moving airplanes may cause foreground aperture problem during detection. Second, various disturbances, especially parallax motion, may cause false detection. Third, airplanes may perform complex motions, which requires a rotation-invariant and scale-invariant tracking algorithm. To tackle these difficulties, we first develop an Improved Gaussian-based Background Subtractor (IPGBBS) algorithm for moving airplane detection. This algorithm adopts a novel strategy for background and foreground adaptation, which can effectively deal with the foreground aperture problem. Then, the detected moving airplanes are tracked by a Primary Scale Invariant Feature Transform (P-SIFT) keypoint matching algorithm. The P-SIFT keypoint of an airplane exhibits high distinctiveness and repeatability. More importantly, it provides a highly rotation-invariant and scale-invariant feature vector that can be used in the matching process to determine the new locations of the airplane in the frame sequence. The method was tested on a satellite video with eight moving airplanes. Compared with state-of-the-art algorithms, our IPGBBS algorithm achieved the best detection accuracy with the highest F1 score of 0.94 and also demonstrated its superiority on parallax motion suppression. The P-SIFT keypoint matching algorithm could successfully track seven out of the eight airplanes. Based on the tracking results, movement trajectories of the airplanes and their dynamic properties were also estimated.
more »
« less
Tracking Multiple Ground Objects Using a Team of Unmanned Air Vehicles
This paper proposes a system architecture for tracking multiple ground-based objects using a team of unmanned air systems (UAS). In the architecture pipeline, video data is processed by each UAS to detect motion in the image frame. The ground-based location of the detected motion is estimated using a geolocation algorithm. The subsequent data points are then process by the recently introduced Recursive RANSAC (R-RANSASC) algorithm to produce a set of tracks. These tracks are then communicated over the network and the error in the coordinate frames between vehicles must be estimated. After the tracks have been placed in the same coordinate frame, a track-to-track association algorithm is used to determine which tracks in each camera correspond to tracks in other cameras. Associated tracks are then fused using a distributed information filter. The proposed method is demonstrated on data collected from two multi-rotors tracking a person walking on the ground.
more »
« less
- Award ID(s):
- 1650547
- PAR ID:
- 10053369
- Date Published:
- Journal Name:
- Lecture notes in control and information sciences
- Volume:
- 474
- ISSN:
- 0170-8643
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
The major challenge posed by the high instantaneous luminosity in the High Luminosity LHC (HL-LHC) motivates efficient and fast reconstruction of charged particle tracks in a high pile-up environment. While there have been efforts to use modern techniques like vectorization to improve the existing classic Kalman Filter based reconstruction algorithms, we take a fundamentally different approach by doing a bottom-up reconstruction of tracks. Our algorithm, called Line Segment Tracking, constructs small track stubs from adjoining detector regions, and then successively links these track stubs that are consistent with typical track trajectories. Since the production of these track stubs is localized, they can be made in parallel, which lends way into using architectures like GPUs and multi-CPUs to take advantage of the parallelism. We establish an implementation of our algorithm in the context of the CMS Phase-2 Tracker which runs on NVIDIA Tesla V100 GPUs, and measure the physics performance and the computing time.more » « less
-
The datafiles in this dataset contain tropical or extratropical cyclone track information. Each datafile name includes the name of city. The datafile contains track information (location and time) for all cyclones that pass within 1000 km of the named city. Each datafile contains a single variable, which is a datatype called: structure in Matlab (or, dictionary). Each entry in the dictionary is an cyclone track. The entries in the dictionary are in chronological order, based on the starting date of the track. The relevant fields in the dictionary are the center latitude, center longitude, and date. The extratropical tracks were identified by applying Mike Bauer’s MCMS tracking tool to ERA5 reanalysis data. The tropical cyclone tracks are from HURDAT2. Full details can be found in the Booth et al. (2021) article referenced with this dataset. For more information contact James Booth: jbooth@ccny.cuny.edumore » « less
-
Integral imaging has proven useful for three-dimensional (3D) object visualization in adverse environmental conditions such as partial occlusion and low light. This paper considers the problem of 3D object tracking. Two-dimensional (2D) object tracking within a scene is an active research area. Several recent algorithms use object detection methods to obtain 2D bounding boxes around objects of interest in each frame. Then, one bounding box can be selected out of many for each object of interest using motion prediction algorithms. Many of these algorithms rely on images obtained using traditional 2D imaging systems. A growing literature demonstrates the advantage of using 3D integral imaging instead of traditional 2D imaging for object detection and visualization in adverse environmental conditions. Integral imaging’s depth sectioning ability has also proven beneficial for object detection and visualization. Integral imaging captures an object’s depth in addition to its 2D spatial position in each frame. A recent study uses integral imaging for the 3D reconstruction of the scene for object classification and utilizes the mutual information between the object’s bounding box in this 3D reconstructed scene and the 2D central perspective to achieve passive depth estimation. We build over this method by using Bayesian optimization to track the object’s depth in as few 3D reconstructions as possible. We study the performance of our approach on laboratory scenes with occluded objects moving in 3D and show that the proposed approach outperforms 2D object tracking. In our experimental setup, mutual information-based depth estimation with Bayesian optimization achieves depth tracking with as few as two 3D reconstructions per frame which corresponds to the theoretical minimum number of 3D reconstructions required for depth estimation. To the best of our knowledge, this is the first report on 3D object tracking using the proposed approach.more » « less
-
Abstract The mitotic spindle is a microtubule‐based machine that pulls the two identical sets of chromosomes to opposite ends of the cell during cell division. The fission yeastSchizosaccharomyces pombeis an important model organism for studying mitosis due to its simple, stereotyped spindle structure and well‐established genetic toolset.S. pombespindle length is a useful metric for mitotic progression, but manually tracking spindle ends in each frame to measure spindle length over time is laborious and can limit experimental throughput. We have developed an ImageJ plugin that can automatically trackS. pombespindle length over time and replace manual or semi‐automated tracking of spindle elongation dynamics. Using an algorithm that detects the principal axis of the spindle and then finds its ends, we reliably track the length of the spindle as the cell divides. The plugin integrates with existing ImageJ features, exports its data for further analysis outside of ImageJ and does not require any programming by the user. Thus, the plugin provides an accessible tool for quantification ofS. pombespindle length that will allow automatic analysis of large microscopy data sets and facilitate screening for effects of cell biological perturbations on mitotic progression.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- The DOI is not currently available.
