Shape context based object recognition and tracking in structured underwater environment

Choi, Hyun Taek

doi:10.1109/igarss.2011.6049204

Cited by 16 publications

(6 citation statements)

References 23 publications

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“…Other developments included methods for effective computing [75], hybridized methods for group tracking [76], multimodal fusion from electro-optical and infrared sensing [77], anomaly detection from known path of travels [78], and track-segment association [79]. Finally, another application versus ground target tracking, was for underwater tracking [80,81].…”

Section: Background On Contextual Tracking Methodsmentioning

confidence: 99%

Contextual Tracking Approaches in Information Fusion

Blasch

Yang²,

García

et al. 2016

Context-Enhanced Information Fusion

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Many information fusion solutions work well in the intended scenarios; but the applications, supporting data, and capabilities change over varying contexts. One example is weather data for electro-optical target trackers of which standards have evolved over decades. The operating conditions of technology changes, sensor/target variations, and the contextual environment can inhibit performance if not included in the initial systems design. In this chapter, we seek to define and categorize different types of contextual information. We describe five contextual information categories that support target tracking: (1) domain knowledge from a user to aid the information fusion process through selection, cueing, and analysis, (2) environment-to-hardware processing for sensor management, (3) known distribution of entities for situation/threat assessment, (4) historical traffic behavior for situation awareness patterns of life (POL), and (5) road information for target tracking and identification. Appropriate characterization and representation of contextual information is needed for future high-level information fusion designs to take advantage of the large data content available for a priori knowledge target tracking algorithm construction, implementation, and application

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Section: Background On Contextual Tracking Methodsmentioning

confidence: 99%

Contextual Tracking Approaches in Information Fusion

Blasch

Yang²,

García

et al. 2016

Context-Enhanced Information Fusion

View full text Add to dashboard Cite

show abstract

“…Other developments included methods for effective computing [72], hybridized methods for group tracking [73], multimodal fusion from electro-optical and infrared sensing [74], anomaly detection from known path of travels [75], and track-segment association [76]. Finally, another application versus ground target tracking, was for underwater tracking [77,78].…”

Section: Contextual Tracking Methods (Review)mentioning

confidence: 99%

Overview of contextual tracking approaches in information fusion

et al. 2013

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Many information fusion solutions work well in the intended scenarios; but the applications, supporting data, and capabilities change over varying contexts. One example is weather data for electro-optical target trackers of which standards have evolved over decades. The operating conditions of: technology changes, sensor/target variations, and the contextual environment can inhibit performance if not included in the initial systems design. In this paper, we seek to define and categorize different types of contextual information. We describe five contextual information categories that support target tracking: (1) domain knowledge from a user to aid the information fusion process through selection, cueing, and analysis, (2) environment-to-hardware processing for sensor management, (3) known distribution of entities for situation/threat assessment, (4) historical traffic behavior for situation awareness patterns of life (POL), and (5) road information for target tracking and identification. Appropriate characterization and representation of contextual information is needed for future high-level information fusion systems design to take advantage of the large data content available for a priori knowledge target tracking algorithm construction, implementation, and application.

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“…Shape as a feature for underwater object detection was used in (Han and Choi, 2011) and (Lee et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

“…Shape as a feature for underwater object detection was used in Han and Choi (2011) and Lee et al (2003). However, this technique is only robust when the target has an a priori known, and invariant shape.…”

mentioning

confidence: 99%

Diver tracking in open waters: A low‐cost approach based on visual and acoustic sensor fusion

Remmas

Chemori

Kruusmaa

2020

Journal of Field Robotics

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The design of a robust perception method is a substantial component towards achieving underwater human-robot collaboration. However, in complex environments such as the oceans, perception is still a challenging issue. Data-fusion of different sensing modalities can improve perception in dynamic and unstructured ocean environments. This work addresses the control of a highly-maneuverable autonomous underwater vehicle for diver tracking based on visual and acoustic signals data fusion measured by low-cost sensors. The underwater vehicle U-CAT tracks a diver using a 3-DOF fuzzy logic Mamdani controller. The proposed tracking approach was validated through open water real-time experiments. Combining acoustic and visual signals for underwater target tracking provides several advantages compared to previously done related research. The obtained results suggest that the proposed * Corresponding author solution ensures effective detection and tracking in poor visibility operating conditions.

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Shape context based object recognition and tracking in structured underwater environment

Cited by 16 publications

References 23 publications

Contextual Tracking Approaches in Information Fusion

Contextual Tracking Approaches in Information Fusion

Overview of contextual tracking approaches in information fusion

Diver tracking in open waters: A low‐cost approach based on visual and acoustic sensor fusion

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