Near-Drowning Early Prediction Technique Using Novel Equations (Neptune) for Swimming Pools

Prakash, Bhaskaran David

doi:10.5121/csit.2018.81812

Cited by 3 publications

(4 citation statements)

References 9 publications

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“…Salehi et al [35] use the HSV threshold mechanism and contour detection function to track swimmers and count time with a camera installed above swimming pools. Prakash et al [18] describe a near-drowning early prediction technique using novel equations. Hou et al [20] propose swimming target detection and tracking technology based on a discrete cosine transform algorithm to analyze motion parameters for drowning detection.…”

Section: Traditional Vision-based Methodsmentioning

confidence: 99%

“…Prakash et al. [18] describe a near‐drowning early prediction technique using novel equations. Hou et al.…”

Section: Related Workmentioning

confidence: 99%

“…The vision‐based method avoids the limitations of wearable devices and mainly extracts features from the swimmer's video to accomplish drowning detection. Many vision‐based studies also detect drowning based on the swimmer's position, time, and speed in the water [12–21], but the drowning person is quiet in the early stage of drowning. So, these methods lack robustness and are not accurate.…”

Section: Introductionmentioning

confidence: 99%

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A video drowning detection device based on underwater computer vision

Liu

et al. 2023

IET Image Processing

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Drowning is a significant public health concern. A video drowning detection algorithm is a helpful tool for finding drowning victims. However, there are three challenges that drowning detection research typically encounters: a lack of actual drowning video data, subtle early drowning traits, and a lack of real time. In this paper, the authors propose an underwater computer vision based drowning detection device composed of embedded AI devices, camera, and waterproof case to solve the above problems. The detection device utilizes the high‐performance computing of Jetson Nano to realize real‐time detection of drowning events through the proposed drowning detection algorithm on the acquired underwater video stream. The proposed drowning detection algorithm primarily consists of two stages: in the first step, to successfully solve the interference of the surroundings and to give a trustworthy basis for video drowning detection, the YOLOv5n network is used to detect the near‐vertical human body based on the characteristics of the drowning person. In the second stage, the authors propose a lightweight drowning detection network (DDN) based on a deep Gaussian model for fast feature vector detection. The lightweight DDN is combined with the Gaussian model to detect anomaly in the high‐level semantic features, which has higher robustness and solves the lack of drowning videos. The experimental results show that the proposed drowning detection algorithm has good comprehensive performance and practical application value.

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Section: Traditional Vision-based Methodsmentioning

confidence: 99%

“…Prakash et al. [18] describe a near‐drowning early prediction technique using novel equations. Hou et al.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A video drowning detection device based on underwater computer vision

Liu

et al. 2023

IET Image Processing

View full text Add to dashboard Cite

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“…However, this method is costly and can restrict the movements of swimmers because of the requirement to wear sensor devices, potentially leading to drowning incidents, which does not align with the original research intention. Considering these issues, methods based on image or video recognition have been proposed, including background subtraction 7 , hue saturation value (HSV) 8 , k-means clustering algorithm 9 , and deep learning 10 – 12 . While recognizing postures through videos or images resolves the issues associated with wearable sensors, the use of airborne optical cameras poses challenges in simultaneously imaging both the water surface and underwater because of the influence of visible light wavelengths.…”

Section: Introductionmentioning

confidence: 99%

Surface and underwater human pose recognition based on temporal 3D point cloud deep learning

Wang,

Wu,

Zhao

2024

Sci Rep

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Airborne surface and underwater human pose recognition are crucial for various safety and surveillance applications, including the detection of individuals in distress or drowning situations. However, airborne optical cameras struggle to achieve simultaneous imaging of the surface and underwater because of limitations imposed by visible-light wavelengths. To address this problem, this study proposes the use of light detection and ranging (LiDAR) to simultaneously detect humans on the surface and underwater, whereby human poses are recognized using a neural network designed for irregular data. First, a temporal point-cloud dataset was constructed for surface and underwater human pose recognition to enhance the recognition of comparable movements. Subsequently, radius outlier removal (ROR) and statistical outlier removal (SOR) were employed to alleviate the impact of noise and outliers in the constructed dataset. Finally, different combinations of secondary sampling methods and sample sizes were tested to improve recognition accuracy using PointNet++. The experimental results show that the highest recognition accuracy reached 97.5012%, demonstrating the effectiveness of the proposed human pose detection and recognition method.

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