Performance Prediction of Feature Aided Trackers using Persistent Sensors
The proposed Phase I program will develop a Bayesian framework for modeling and predicting track uncertainty and performance as a function of OCs and integrate this framework with algorithms for tracking vehicles in WAMI. The proposed Performance and Uncertainty Modeling and Prediction for Tracking (PUMP-T) framework is comprised of the following key elements: (1) explicit extraction of operating conditions (OCs), (2) calculation of information-theoretic metrics for quantifying uncertainty in track posterior probability density functions (PDFs), (3) sparse Bayesian regression algorithms for modeling track uncertainty as a function of OCs, (4) Bayesian semi-supervised regression/classification algorithms for modeling track performance as a function of OCs, (5) an offline process for training the uncertainty and performance models, and (6) an online process for predicting track uncertainty and performance and directly validating the models in the context of the observed data. The underlying prediction models driving the PUMP-T framework will be founded on posterior PDFs over track states inferred within a rigorous Bayesian framework. In Phase I, the PUMP-T framework will be designed to be agnostic to the specific tracking algorithm employed such that alternate tracking algorithms can be integrated and evaluated within the PUMP-T framework. BENEFIT: While the PUMP-T framework will be leveraged for video tracking applications in the proposed program, the framework easily generalizes to other exploitation algorithms (e.g. detection and classification) and other sensor modes. Medical device manufacturers are developing cutting edge sensors leading to revolutionary advances in diagnosis of a variety of diseases, especially cancer. These devices have the ability to gather a significant amount of data, much more than a physician or technician can handle alone. Therefore, automated processing, including rigorous understanding of uncertainty and performance predictions, is necessary for realizing the full potential of these devices. While the video exploitation software leveraged under this SBIR offer significant impact on a broad spectrum of DoD and IC applications today and in the future, the exploitation software also offers significant benefit to several existing and new commercial markets, including municipal public safety, traffic analysis and flow optimization, mining of video for targeted advertising, corporate security, and mining of surveillance cameras for retail applications. SIG has had discussions with companies and venture capital interests that participate in such markets and believes that the exploitation products of this SBIR would benefit these video content extraction markets.
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Signal Innovations Group, Inc.
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