Nick A. Mould

Infrared Target Tracking with AM-FM Consistency Checks

Challenging infrared data sequences such as the well-known AMCOM closure sequences are characterized by highly nonstationary evolutionary target and clutter signatures, poor target-to- clutter ratios, and complex kinematics arising from both the target motion and the motion of the sensor platform itself. In such cases, track consistency checks can provide a valuable means for detecting an imminent track loss. In this paper, we consider a simple target model with a correlation-based detection process and a straightforward SIR particle filter track processor. We show that the performance of the track processor can be dramatically improved by incorporating modulation domain consistency checks to identify failure in the correlation-based detection process. This strategy results in a robust dual-domain tracker that, despite the simplicity of its state model, delivers superior tracking performance against the very difficult AMCOM sequences.

Dual domain auxiliary particle filter with integrated target signature update

For the first time, we formulate an auxiliary particle filter jointly in the pixel domain and modulation domain for tracking infrared targets. This dual domain approach provides an information rich image representation comprising the pixel domain frames acquired directly from an imaging infrared sensor as well as 18 amplitude modulation functions obtained through a multicomponent AM-FM image analysis. The new dual domain auxiliary particle filter successfully tracks all of the difficult targets in the well-known AMCOM closure sequences in terms of both centroid location and target magnification. In addition, we incorporate the template update procedure into the particle filter formulation to extend previously studied dual domain track consistency checking mechanism far beyond the normalized cross correlation (NCC) trackers of the past by explicitly quantifying the differences in target signature evolution between the modulation and pixel domains. Experimental results indicate that the dual domain auxiliary particle filter with integrated target signature update provides a significant performance advantage relative to several recent competing algorithms.

Online Consistency Checking for AM-FM Target Tracks

We compute AM-FM models for infrared video frames depicting military targets immersed in structured clutter backgrounds. We show that independent correlation based detection processes can be implemented in the pixel and modulation domains and used to construct useful online track consistency checks that indicate when the detection process has been degraded due to nonstationary evolution of the target signature. Throughout the paper, we use the well-known AMCOM closure sequences as exemplars.

A conservative scene model update policy

In this paper, we present a new pixel-level scene model for segmenting video into foreground and background structure. The design of the model is largely influenced by several recently reported stochastic background models that have been shown to significantly outperform traditional deterministic techniques. In contrast to existing nonparametric scene models, we propose a learning algorithm that integrates new information into the models by replacing the most significant outlying values with respect to the current sample collections. Outliers are identified using a variable bandwidth kernel density estimation (KDE) procedure. We demonstrate the superiority of our model against a recent state-of-the-art video segmentation system and compare and contrast the theoretical aspects of our model with a wide variety of existing techniques, and well known video segmentation challenges.

A stochastic learning algorithm for pixel-level background models

A new stochastic learning algorithm for use in nonparametric pixel-level background models is presented in this paper. For the first time, we propose the use of kernel density estimation (KDE) techniques in the model update step to identify outliers within the pixel-level sample collections and replace them with with recently observed background image features. A neighborhood diffusion process that improves on recently reported scene model learning techniques is presented, wherein information sharing between similarly structured adjacent background models is encouraged to promote spatial consistency within localized image regions. We demonstrate the superiority of the proposed algorithm by comparison with the state-of-the-art ViBe system using the well known percentage correct classification (PCC) statistic and a new figure of merit, probability correct classification (PrCC), presented here for the first time.

Dynamic configuration steering for a reconfigurable superscalar processor

A new dynamic vector approach for the selection and management of the configuration of a reconfigurable superscalar processor is proposed. This new method improves on previous work that used steering vectors to guide the selection of functional units to be loaded into the processor. Dependencies among instructions in the instruction buffer are analyzed to enable a new scoring method. The dynamic vector technique is shown to reduce the amount of reconfiguration required while preserving execution resources. Simulation results reveal that, given enough configurable space, the configuration of the processor approaches a stable state.

Quantifying infrared target signature evolution using AM-FM features

In this paper we combine a new method of measuring infrared target signature evolution with current research and developmental tracking algorithms. Thermal images are decomposed by a set of Gabor filters and demodulated to produce a set of spatiospectrally localized AM-FM functions corresponding to oriented texture regions from within the original image. Critical updates are detected and issued to a particle filter based tracker, operating only on a modulation domain target model, by applying an empirically determined threshold to a new target evolution measurement introduced in this paper. We achieve results comparable to several other theoretical tracking algorithms at a significantly reduced computational cost by eliminating the need to perform parallel tracking in both the pixel and modulation domains.

Continuity and Change in the Operational Dynamics of the Islamic State

In this article we estimate the influence of leadership changes on the operational dynamics associated with terrorist attacks conducted by the Islamic State and its predecessors. Because the focus of our research is empirical, the study uses data for 2,131 successful attacks between October 2002 and December 2014 to examine differentials in operational tempo, attack severity, primary tactics employed, and principal targets. The data are aggregated on a monthly basis to estimate the probabilities This article is available in Journal of Strategic Security: https://scholarcommons.usf.edu/jss/vol10/iss1/5 associated with specific attack sequences in terms of the following primary tactics: (1) firearms, (2) explosives, (3) hostage-taking/ kidnapping, and (4) attacks involving combinations of (1), (2), and/or (3). The analysis is placed in a broad historical and strategic context in order to explore two key issues: (1) The effect of leadership change on terrorist group activity and (2) The implications for counterterrorism and counterinsurgency efforts. Our analysis reveals a myriad of conceptual, theoretical, and policy implications. Disclaimer The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Acknowledgements This research was supported by the Defense Intelligence Agency, Grant # HHM402-14-1-007 (PI: Regens). The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of DIA or the US government. This article is available in Journal of Strategic Security: https://scholarcommons.usf.edu/jss/vol10/iss1/5

Probabilistic Graphical Modeling of Terrorism Threat Recognition Using Bayesian Networks and Monte Carlo Simulation

The objective of this study was to identify and model the comprehension and decision making of law enforcement personnel with respect to terrorism-centric behaviors. In this study, 44 participants provided 1,496 judgments ranked on an 11-point Likert-type suspicion scale about individual text-based scenario components emulating real-world events encountered during routine policing. The data were analyzed to assess the influence of jurisdiction, training, experience, and terrorism familiarity on the recognition of terrorism-centric behaviors. Measurements of those factors were used to formulate a Bayesian network, and Monte Carlo simulation was performed to estimate their effect on terrorism-centric cognitive judgment and decision making, defined by the response to a wide variety of simulated terrorism-centric behaviors using text-based scenarios.

Neighborhood-level learning techniques for nonparametric scene models

A new stochastic learning algorithm for use in nonparametric pixel-level background models is presented in this paper. For the first time, we propose the use of kernel density estimation techniques in the model update step to identify outliers within the pixel-level sample collections and replace them with recently observed background pixel values. A neighborhood diffusion process that improves on recently reported scene model learning techniques is presented, wherein information sharing between similarly structured adjacent background models is encouraged to promote spatial consistency within localized image regions. We demonstrate the superiority of the proposed algorithm in comparison with the state-of-the-art visual background extraction system using the well-known percentage correct classification statistic and a new figure of merit, probability correct classification, presented here for the first time.