There are hosts of target tracking algorithm approaches, each valued with respect to the scenario operating conditions (e.g.
sensors, targets, and environments). Due to the application complexity, no algorithm is general enough to be widely
applicable, nor is a tailored algorithm able to meet variations in specific scenarios. Thus, to meet real world goals,
multitarget tracking (MTT) algorithms need to undergo performance assessment for (a) bounding performance over
various operating conditions, (b) managing expectations and applicability for user acceptance, and (c) understanding the
constraints and supporting information for reliable and robust performance. To meet these challenges, performance
assessment should strive for three goals: (1) challenge problem scenarios with a rich variety of operating conditions, (2) a
standard, but robust, set of metrics for evaluation, and (3) design of experiments for sensitivity analysis over parameter
variation of models, uncertainties, and measurements.
Image registration in wide area motion imagery (WAMI) is a critical problem that is required for target tracking, image fusion, and situation awareness. The high resolution, extremely low frame rate, and large camera motion in such videos; however, introduces challenging constraints that distinguish the task from
traditional image registration from such sesnors as full motion video (FMV). In this study, we propose to
use the feature-based approach for the registration of wide area surveillance imagery. Specifically, we extract Speeded Up Robust Feature (SURF) feature points for each frame. After that, a kd-tree algorithm is adopted to match the feature points of each frame to the reference frame. Then, we use the RANdom SAmple Consensus (RANSAC) algorithm to refine the matching results. Finally, the refined matching point pairs are used to estimate the transformation between frames. The experiments are conducted on the Columbus Large Image Format (CLIF) dataset. The experimental results show that the proposed approach is very efficient for the wide area motion imagery registration.
With the advent of new technology in wide-area motion imagery (WAMI) and full-motion video (FMV), there is a
capability to exploit the imagery in conjunction with other information sources for improving confidence in detection,
tracking, and identification (DTI) of dismounts. Image exploitation, along with other radar and intelligence information
can aid decision support and situation awareness. Many advantages and limitations exist in dismount tracking analysis
using WAMI/FMV; however, through layered management of sensing resources, there are future capabilities to explore
that would increase dismount DTI accuracy, confidence, and timeliness. A layered sensing approach enables commandlevel
strategic, operational, and tactical analysis of dismounts to combine multiple sensors and databases, to validate DTI
information, as well as to enhance reporting results. In this paper, we discuss WAMI/FMV, compile a list of issues and
challenges of exploiting the data for WAMI, and provide examples from recently reported results. Our aim is to provide a
discussion to ensure that nominated combatants are detected, the sensed information is validated across multiple
perspectives, the reported confidence values achieve positive combatant versus non- combatant detection, and the related
situational awareness attributes including behavior analysis, spatial-temporal relations, and cueing are provided in a timely
and reliable manner to stakeholders.
Proc. SPIE. 5809, Signal Processing, Sensor Fusion, and Target Recognition XIV
KEYWORDS: Cognitive modeling, Detection and tracking algorithms, Data modeling, Databases, Computer simulations, Data processing, Signal processing, Algorithm development, Systems modeling, Data fusion
This paper introduces the merge at a point (MAP) algorithm to detect the vehicles convoys whose destination locations are unknown. The algorithm will predict the merged vehicles identification numbers in an iterative manner. We applied this method using the simulated Ground Moving Target Indicator (GMTI) data. The technique is similar to the dead reckoning and Kalman filtering algorithms. This algorithm consists of following procedures: 1) approximates the destination locations for each vehicle using its tracks, 2) validates what vehicles are going to merge at these predicted destination locations using the minimum error solution (MES), and 3) predicts the future destination locations where the vehicles will be merged at for the next iteration. This algorithm will be iteratively processed until predicted destination locations converge. We can use this algorithm to associate the vehicles that will merge to some unknown destination locations. It also has the potential to identify the convoy names and the threats associated with these vehicle groups.
This paper describes a novel technique to detect military convoy’s moving patterns using the Ground Moving Target Indicator (GMTI) data. The specific pattern studied here is the moving vehicle groups that are merging onto a prescribed location. The algorithm can be used to detect the military convoy’s identity so that the situation can be assessed to prevent hostile enemy military advancements. The technique uses the minimum error solution (MES) to predict the point of intersection of vehicle tracks. Comparing this point of intersection to the prescribed location it can be determined whether the vehicles are merging. Two tasks are performed to effectively determine the merged vehicle group patterns: 1) investigate necessary number of vehicles needed in the MES algorithms, and 2) analyze three decision rules for clustering the vehicle groups. The simulation has shown the accuracy (88.9% approx.) to detect the vehicle groups that merge at a prescribed location.
This paper presents a novel approach to: 1) distinguish military vehicle groups, and 2) identify names of military vehicle convoys in the level-2 fusion process. The data is generated from a generic <i>Ground Moving Target Indication </i>(GMTI) simulator that utilizes <i>Matlab</i> and <i>Microsoft Access</i>. This data is processed to identify the convoys and number of vehicles in the convoy, using the <i>minimum timed distance variance </i>(MTDV) measurement. Once the vehicle groups are formed, convoy association is done using hypothesis techniques based upon <i>Neyman Pearson </i>(NP) criterion. One characteristic of NP is the low error probability when <i>a-priori </i>information is unknown. The NP approach was demonstrated with this advantage over a <i>Bayesian</i> technique.