Spatial Search via Adaptive Submodularity and Deep Learning

Tsai, Yu-Chung; Lu, Bingxian; Tseng, Kuo-Shih

doi:10.1109/ssrr.2019.8848962

Cited by 7 publications

(4 citation statements)

References 3 publications

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“…In [33], another lightweight object detection model was tested on a dataset including people and objects in water. In [34], the authors proposed an adaptive submodularity and deep learning-based spatial search method for detecting humans with a UAV. In [35], the authors performed human detection for SAR operations using images from different in-land environments.…”

Section: Related Workmentioning

confidence: 99%

On Automatic Person-in-Water Detection for Marine Search and Rescue Operations

Taipalmaa,

Raitoharju,

Queralta

et al. 2024

IEEE Access

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In marine search and rescue missions, the objective is to find a missing person in the water. Time is a critical factor in the identification of the missing person, as any delay in locating them can have life-threatening consequences. Autonomous unmanned aerial vehicles (UAVs) possess the potential to help in the search task by providing a bird's-eye view helping to cover larger areas faster. Therefore, it is very important that UAVs can efficiently and accurately detect persons in the water. This work studies automatic person detection in the water from a UAV. We performed experiments on both lakes and sea near Turku, Finland and captured videos of people in the water from various altitudes and different viewing angles. Our person-in-water detection tests focus on important factors that have not received sufficient attention in prior studies: evaluation metrics and detection thresholds, the impact and use of different bounding box sizes, multi-frame detection and performance in unseen environments. We provide analysis of the suitability of different approaches for the person detection task and we also publish our training and testing data that includes over 72500 frames. To the best of our knowledge, this is the largest publicly available person-inwater detection dataset.INDEX TERMS Search and rescue (SAR), person-in-water, unmanned aerial vehicle (UAV), object detection, deep learning (DL), dataset

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Section: Related Workmentioning

confidence: 99%

On Automatic Person-in-Water Detection for Marine Search and Rescue Operations

Taipalmaa,

Raitoharju,

Queralta

et al. 2024

IEEE Access

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“…In [201], authors observed a slight decrease on the accuracy while the new network was faster comparing to the old structure. In [203], an adaptive submodularity and deep learning-based spatial search method for detecting humans with UAV in a 3D environment was proposed.…”

Section: B Object Detectionmentioning

confidence: 99%

Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision

et al. 2020

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Search and rescue (SAR) operations can take significant advantage from supporting autonomous or teleoperated robots and multi-robot systems. These can aid in mapping and situational assessment, monitoring and surveillance, establishing communication networks, or searching for victims. This paper provides a review of multi-robot systems supporting SAR operations, with system-level considerations and focusing on the algorithmic perspectives for multi-robot coordination and perception. This is, to the best of our knowledge, the first survey paper to cover (i) heterogeneous SAR robots in different environments, (ii) active perception in multi-robot systems, while (iii) giving two complementary points of view from the multi-agent perception and control perspectives. We also discuss the most significant open research questions: shared autonomy, sim-to-real transferability of existing methods, awareness of victims' conditions, coordination and interoperability in heterogeneous multi-robot systems, and active perception. The different topics in the survey are put in the context of the different challenges and constraints that various types of robots (ground, aerial, surface, or underwater) encounter in different SAR environments (maritime, urban, wilderness, or other post-disaster scenarios). The objective of this survey is to serve as an entry point to the various aspects of multi-robot SAR systems to researchers in both the machine learning and control fields by giving a global overview of the main approaches being taken in the SAR robotics area.

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“…Greedy algorithms can give solutions with theoretical guarantees for submodular maximization problems under different constraints (e.g., cardinality [1], knapsack [3], and routing [4]). The applications include collecting lake information using multiple robots [11], search in indoor environments via graphical models [12], search in indoor environments via probabilistic models [10], search for humans [13], and collecting wireless information using UAVs [14].…”

Section: Submodularitymentioning

confidence: 99%

People search via deep compressed sensing techniques

Tsai

Tseng

2022

Robotica

Self Cite

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People search can be reformulated as submodular maximization problems to achieve solutions with theoretical guarantees. However, the number of submodular function outcome is $2^N$ from N sets. Compressing functions via nonlinear Fourier transform and spraying out sets are two ways to overcome this issue. This research proposed the submodular deep compressed sensing of convolutional sparse coding (SDCS-CSC) and applied the Topological Fourier Sparse Set (TFSS) algorithms to solve people search problems. The TFSS is based on topological and compressed sensing techniques, while the CSC is based on DCS techniques. Both algorithms enable an unmanned aerial vehicle to search for the people in environments. Experiments demonstrate that the algorithms can search for the people more efficiently than the benchmark approaches. This research also suggests how to select CSC or TFSS algorithms for different search problems.

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Spatial Search via Adaptive Submodularity and Deep Learning

Cited by 7 publications

References 3 publications

On Automatic Person-in-Water Detection for Marine Search and Rescue Operations

On Automatic Person-in-Water Detection for Marine Search and Rescue Operations

Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision

People search via deep compressed sensing techniques

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