This paper addresses the problem of tracking a dim moving point target in a sequence of IR images. The proposed tracking system, based on the track-before-detect (TBD) approach, is designed to track and detect dim maneuvering targets from an image sequence under low SNR conditions; a dynamic programming algorithm (DPA) is used to process the frames in the sequence. This paper deals with the practical issues of setting the parameters of our algorithm to maximize tracking capability. In our flexible approach, we are able to accommodate different levels of noise and different target velocities and mobilities. Our algorithm is tested on real data to determine its efficacy.
In this paper, a novel tracking system based on the Track Before Detect Approach (TBD) is introduced. Each IR sequence is preprocessed first by using a whitening algorithm. This stage is used to reject clutter and emphasize targets. Afterwards, a Dynamic Programming Algorithm (DPA) is used for numerous frames in the sequence. The algorithm, a derivative of the well known Viterbi Algorithm, gives each pixel in the image a score based on the current frame and the previous one. By doing so, the temporal behavior difference between targets, clutter and noise is utilized to distinguish between them; we give scores accordingly. At the end of this stage, after the last frame of the IR sequence has been processed, the pixel with the highest accumulated score is chosen as the Target, and its path is found. The paper deals with the different issues characterizing the system, enabling it to have versatility over a wide range of scenes. Future work will involve the use of the system for tracking of targets in hyperspectral cubes.
In this article, we consider the problem of tracking a point target moving against a background of sky and clouds. The proposed solution consists of three stages: the first stage transforms the hyperspectral cubes into a twodimensional (2D) temporal sequence using known point target detection acquisition methods; the second stage involves the temporal separation of the 2D sequence into sub-sequences and the usage of a variance filter (VF) to detect the presence of targets using the temporal profile of each pixel in its group, while suppressing clutterspecific influences. This stage creates a new sequence containing a target with a seemingly faster velocity; the third stage applies the Dynamic Programming Algorithm (DPA) that tracks moving targets with low SNR at around pixel velocity. The system is tested on both synthetic and real data.
Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the burden, to Department of Defense, Washington Headquarters Services, Directorate for Information PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)Ben-Gurion University of the Negev PO Box 653 Beer-Sheva 84 105 Israel PERFORMING ORGANIZATION REPORT NUMBERN/A SPONSOR/MONITOR'S ACRONYM(S) 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)EOARD PSC 821 BOX 14 FPO AE 09421-0014 SPONSOR/MONITOR'S REPORT NUMBER(S)Grant 03-3077 DISTRIBUTION/AVAILABILITY STATEMENTApproved for public release; distribution is unlimited. SUPPLEMENTARY NOTES ABSTRACTThis report results from a contract tasking Ben-Gurion University of the Negev as follows: The Grantee will investigate developing a full target detection module which analyzes a temporal sequence of hyperspectral datacubes for the acquisition of infrared targets. They will test this module as a function of target strength and background clutter to test for its ruggedness in its ability to detect targets with low false alarm rates. They will document its improvement over systems which use the temporal and hyperspectral data separately. This is the final report. SUBJECT TERMS AbstractThe scope of this project addresses the problem of tracking a dim moving point target from a sequence of hyperspectral cubes. The resulting tracking algorithm is useful for many staring technologies such as the ones used in space surveillance and missile tracking applications. In these applications, the images consist of targets moving at sub-pixel velocity and noisy background consisting of evolving clutter and noise. The demand for a low false alarm rate (FAR) on one hand and a high probability of detection (P D ) on the other makes the tracking a challenging task. The use of hyperspectral imagesshould be superior to current technologies using broadband IR images due to the ability of exploiting simultaneously two target specific properties: the spectral target characteristics and the time dependent target behavior.The proposed solution consists of three stages: the first stage transforms the hyperspectral cubes into a two dimensional sequence, using known point target detection acquisition methods; the second stage involves a temporal separation of the 2D sequence into sub-sequences and the usage of a variance filter (VF) to detect the presence of targets from the temporal profile of each pixel in each group, while suppressing clutter specific influences. This stage creates a new sequence containing a target with a seemingly faster velocity; the third stage applies the Dynamic Programming Algorithm (DPA) that proves to be a very effective algorithm for the tracking of moving ...
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