QuEST for information fusion

Blasch, Erik; Rogers, Steven K.; Culbertson, Jared; Rodríguez, Andrés; Fenstermacher, Laurie; Patterson, Robert

doi:10.1109/naecon.2014.7045805

Cited by 6 publications

(5 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also, the obtained tracking results for the multiple moving targets tracking present in the airborne video datasets indicate that the CAV system demonstrates robust detection and tracking in real-time and under realistic conditions. Future work will include multi-mode tracking, image quality assessment [60], integration with text data for multimedia analysis [61], and multiple user applications viewing the same imagery for distributed applications [62].…”

Section: Discussionmentioning

confidence: 99%

A cloud infrastructure for target detection and tracking using audio and video fusion

Liu

Blasch

et al. 2015

2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Self Cite

View full text Add to dashboard Cite

This paper presents a Cloud-based architecture for detecting and tracking multiple moving targets from airborne videos combined with the audio assistance, which is called Cloudbased Audio-Video (CAV) fusion. The CAV system innovation is a method for user-based voice-to-text color feature descriptor track matching with an automated hue feature extraction from image pixels. The introduced CAV approach is general purpose for detecting and tracking different valuable targets' movement for suspicious behavior recognition through multi-intelligence data fusion. Using Cloud computing leads to real-time performance as compared a single machine workflow. The obtained multiple moving target tracking results from airborne videos demonstrate that the CAV approach provides improved frame rate, enhanced detection, and real-time tracking and classification performance under realistic conditions.

show abstract

Section: Discussionmentioning

confidence: 99%

A cloud infrastructure for target detection and tracking using audio and video fusion

Liu

Blasch

et al. 2015

2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Self Cite

View full text Add to dashboard Cite

show abstract

“…To meet this need we propose utilizing a relatively new approach: Qualia-based Exploitation of Sensing Technology (QuEST). The overarching goal of QuEST is to design an IAM [7], [8] that can effectively partner with human operators (facilitating tight coupling between human and machine). QuEST is composed of three key modules: 1) agents (sense and act upon an environment), 2) qualia (agent-experienced data and constructs derived from sensed data), and 3) a dualprocess framework that posits two qualitatively different yet related processing modes (see Figure 1).…”

Section: Questmentioning

confidence: 99%

Poster: Qualia Exploitation of Sensing Technology (QuEST) for vehicular network optimization

Hawkes

Bihl

Rogers

2016

2016 IEEE Vehicular Networking Conference (VNC)

Self Cite

View full text Add to dashboard Cite

Qualia-based Exploitation of Sensing Technology (QuEST) is a dual process framework that leverages what is known about human neurophysiological and neuropsychological processes to create an artificial cognitive exoskeleton functioning similarly to the human mind. In this paper, we present a quick QuEST overview and a visionary approach using QuEST methods that can improve cognitive V2V network resistance to hacking. QuEST tenets and designs have been used successfully in cyber security, facial recognition, and cancer detection; thus V2V information security in the open internet context can be enhanced via QuEST. Of note, QuEST's focus is on intelligence amplification (IA) versus artificial intelligence (AI) and developing a machine architecture which closes the loop between human and machine.

show abstract

“…Decision support architectures bring together geo-information fusion, semantic content, and collaborative decision making through such technologies as message oriented middleware, storage of heterogeneous information, dynamic data containers, and conceptual schemas as demonstrated for industrial drilling operations [67]. Recent results include multi-intelligence data visualization [68], decisions-to-data (versus data to decisions) [69], and Qualia for exploitation [70]. The elements of information fusion aid a user in building the narrative [71].…”

Section: Decision Supportmentioning

confidence: 99%

One decade of the Data Fusion Information Group (DFIG) model

Blasch

2015

SPIE Proceedings

Self Cite

View full text Add to dashboard Cite

The revision of the Joint Directors of the Laboratories (JDL) Information Fusion model in 2004 discussed information processing, incorporated the analyst, and was coined the Data Fusion Information Group (DFIG) model. Since that time, developments in information technology (e.g., cloud computing, applications, and multimedia) have altered the role of the analyst. Data production has outpaced the analyst; however the analyst still has the role of data refinement and information reporting. In this paper, we highlight three examples being addressed by the DFIG model. One example is the role of the analyst to provide semantic queries (through an ontology) so that vast amount of data available can be indexed, accessed, retrieved, and processed. The second idea is reporting which requires the analyst to collect the data into a condensed and meaningful form through information management. The last example is the interpretation of the resolved information from data that must include contextual information not inherent in the data itself. Through a literature review, the DFIG developments in the last decade demonstrate the usability of the DFIG model to bring together the user (analyst or operator) and the machine (information fusion or manager) in a systems design.

show abstract

QuEST for information fusion

Cited by 6 publications

References 44 publications

A cloud infrastructure for target detection and tracking using audio and video fusion

A cloud infrastructure for target detection and tracking using audio and video fusion

Poster: Qualia Exploitation of Sensing Technology (QuEST) for vehicular network optimization

One decade of the Data Fusion Information Group (DFIG) model

Contact Info

Product

Resources

About