KEYWORDS: Kinematics, Detection and tracking algorithms, Roads, Data modeling, Video, Scattering, Matrices, Monte Carlo methods, Motion models, Mathematics
Group moving targets are number of targets independently moving in a physical space but keeping their relative order or
pattern invariant. The up to date state-of-the-art multi-target tracking (MTT) data association methods (GNN,JPDA,MHT)
are easily fail on group targets tracking problems, since the tracker-to-observation ambiguity cannot be resolved if only
using the individual track to observation information. A hypergraph G is represented by G = {V,E}, where V is a set of
elements called nodes or vertices, E is a set of non-empty subsets containing d-tuple of vertices called hyperedges. It can
be used as a new mathematic tool to represent a group of moving targets if we let each target be a vertex and a d-target
subset be an hyperedge. Under this representation, this paper reformulates the traditional MTT data association problem as
an hypergraph matching one between the hypergraphs formed from tracks and observations, and shows that the traditional
approach (only uses the vertex-to-vertex information) which is a special case under the proposed framework. In addition
to the vertex-to-vertex information, since the hyperedge-to-hyperegde information is also used in building the assignment
matrix, the hypergraph matching based algorithms give better performance than that from the traditional methods in group
target tracking problems. We demonstrate the declaration from simulations as well as video based geotracking examples.
KEYWORDS: Video, Roads, Video surveillance, Detection and tracking algorithms, Surveillance, Target detection, Sensors, Data modeling, Geographic information systems, Databases
This paper presents a relational graph based approach to track thousands of vehicles from persistent wide area airborne
surveillance (WAAS) videos. Due to the low ground sampling distance and low frame rate, vehicles usually have small
size and may travel a long distance between consecutive frames, WAAS videos pose great challenges to correct
associate existing tracks with targets. In this paper, we explore road structure information to regulate both object based
vertex matching and pair-wise edge matching schemes in a relational graph. The proposed relational graph approach
then unifies these two matching schemes into a single cost minimization framework to produce a quadratic optimized
association result. The experiments on hours of real WAAS videos demonstrate the relational graph matching framework
effectively improves vehicle tracking performance in large scale dense traffic scenarios.
In the intelligence community, aerial video has become one of the fastest growing data sources and it has been
extensively used in intelligence, surveillance, reconnaissance, tactical and security applications. This paper
presents a tracking approach to detect moving vehicles and person in such videos taken from aerial platform.
In our approach, we combine the layer segmentation approach with background stabilization and post-tracking
refinement to reliably detect small moving objects at the relatively low processing speed. For each individual
moving object, a corresponding layer is created to maintain an independent appearance and motion model
during the tracking process. After the online tracking process, we apply a post-tracking refinement process to
link the track fragments into a long consistent track ID to further reduce false alarm and increase detection rate.
Furthermore, a vehicle and person classifier is also integrated into the approach to identify the moving object
categories. The classifier is based on image histogram of gradient (HOG), which is more reliable to illumination
variation or camera automatic gain change. Finally, we report the results of our algorithms on a large scale of
EO and IR data set collected from VIVID program, and the results show that our approach achieved a good
and stable tracking performance on the data set that is more than eight hours.
This paper describes a system for automatically detecting potential targets (that pop-up or move into view)
and to cue the operator to potential threats. Detection of independently moving targets from a moving ground
vehicle is challenging due to the strong parallax effects caused by the camera motion close to the 3D structure in
the environment. We present a 3D approach for detecting and tracking such independently moving targets with
multiple monocular cameras. In our approach, we first recover the camera position and orientation by employing a
visual odometry method. Next, using multiple consecutive frames with the estimated camera poses, the structure
of the scene at the reference frame is explicitly recovered by a motion stereo approach, and corresponding optical
flow fields between the reference frame and other frames are also estimated. Third, an advanced filter is designed
by combining second order differences between 3D warping and optical flow warping to distinguish the moving
object from parallax regions. We present results of the algorithm on data collected with an eight-camera system
mounted on a vehicle under multiple scenarios that include moving and pop-up targets.
This paper describes a novel approach to automatically recognize the target based on a view morphing database constructed by our multi-view morphing algorithm. Instead of using single reference image, a set of images or a video sequence is used to construct the reference database, where these images are re-organized by a triangulation of viewing sphere. At the vertex of each triangle, one image is stored in the database as the reference view from a specific viewing direction. For each triangle, our tri-view morphing algorithm can synthesize a high quality image for an arbitrary novel viewpoint amongst three neighboring reference images, and the barycentric blending scheme guarantees the seamless transitions between each neighboring triangles. Using the synthesized images, we apply appearance based recognition technique to recognize the target. In addition, using the proposed method, the pose of the object or camera motion can be approximately estimated. Several examples are demonstrated in the experiments to show that our approach is effective and promising.
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