Video observations for cloud activity-based intelligence (VOCABI)

Blasch, Erik; DiBona, Phillip; Czajkowski, Michael; Barry, Kevin; Rimey, Ray; Freeman, J. C.; Newman, K. W.; Aved, Alexander; Hinman, Mike

doi:10.1109/naecon.2014.7045804

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…Future efforts would include comprehensive operations of the battlespace. Four areas identified as supporting the next generation of BMC2-C4I systems are (1) big data analytics that combines assessment and visualization for content-based understanding, (2) machine intelligence both in automation and autonomy for information fusion [97,98] (3) Battlespace on demand that orchestrates services for the continuity of operations of context-based effects-based awareness [99], and (4) C2 agility that provides robust dynamic operations though multi-intelligence services collaborations that are synchronized for efficiency to meet time-dominant fusion requirements.…”

Section: Discussionmentioning

confidence: 99%

Agile battle management efficiency for command, control, communications, computers and intelligence (C4I)

Blasch

Bélanger

2016

SPIE Proceedings

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Various operations such as civil-military co-operation (CIMIC) affairs require orchestration of communications, assets, and actors. A key component includes technology advancements to enable coordination among people and machines the ability to know where things are, who to coordinate with, and open and consistent lines of communication. In this paper, we explore concepts of battle management (BM) to support high-tempo emergency response scenarios such as a disaster action response team (DART). Three concepts highlighted of agile battle management (ABM) include source orchestration (e.g., sensors and domains), battle management language (BML) development (e.g., software and ontologies), and command and control (C2) coordination (e.g., people and visualization); which require correlation and de-confliction. These concepts of ABM support the physical, information, and cognitive domains for efficient command, control, communications, and information (C3I) to synchronize data and people for efficient and effective operations.

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Section: Discussionmentioning

confidence: 99%

Agile battle management efficiency for command, control, communications, computers and intelligence (C4I)

Blasch

Bélanger

2016

SPIE Proceedings

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“…Cloud and parallel computation has become increasingly important for computer vision and image processing systems [ 23 , 24 ]. A Cloud-based framework uses Cloud computing, which is constructed within high performance clusters to include the combination of CPUs and GPUs [ 25 , 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

A Real-Time High Performance Computation Architecture for Multiple Moving Target Tracking Based on Wide-Area Motion Imagery via Cloud and Graphic Processing Units

Liu

Wei

Chen

et al. 2017

Sensors

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This paper presents the first attempt at combining Cloud with Graphic Processing Units (GPUs) in a complementary manner within the framework of a real-time high performance computation architecture for the application of detecting and tracking multiple moving targets based on Wide Area Motion Imagery (WAMI). More specifically, the GPU and Cloud Moving Target Tracking (GC-MTT) system applied a front-end web based server to perform the interaction with Hadoop and highly parallelized computation functions based on the Compute Unified Device Architecture (CUDA©). The introduced multiple moving target detection and tracking method can be extended to other applications such as pedestrian tracking, group tracking, and Patterns of Life (PoL) analysis. The cloud and GPUs based computing provides an efficient real-time target recognition and tracking approach as compared to methods when the work flow is applied using only central processing units (CPUs). The simultaneous tracking and recognition results demonstrate that a GC-MTT based approach provides drastically improved tracking with low frame rates over realistic conditions.

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“…Cloud technology has become increasingly important for multisensory and image processing systems [35,36,37]. A Cloud-based framework uses Cloud computing [38], is built using Xenserver [39].…”

Section: Introductionmentioning

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)

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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.

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Video observations for cloud activity-based intelligence (VOCABI)

Cited by 4 publications

References 27 publications

Agile battle management efficiency for command, control, communications, computers and intelligence (C4I)

Agile battle management efficiency for command, control, communications, computers and intelligence (C4I)

A Real-Time High Performance Computation Architecture for Multiple Moving Target Tracking Based on Wide-Area Motion Imagery via Cloud and Graphic Processing Units

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

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