RETRACTED: Swimming target detection and tracking technology in video image processing

Hou, Jianbin; Li, Baoguo

doi:10.1016/j.micpro.2020.103535

Cited by 21 publications

(9 citation statements)

References 6 publications

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“…If the coordinates are not known, it can use the three points in the known image and their mapping points to obtain the parameter values and use the principle of linear distortion. Then, use formulas (18) and (19) to calculate the coordinates ðx 0 , y 0 Þ [23].…”

Section: Special Circumstancesmentioning

confidence: 99%

ARToolKit Target Tracking and Animation Fusion Technology Based on Edge Computing and Augmented Reality

Liu

Guo

2023

Wireless Communications and Mobile Computing

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Since the beginning of the 21st century, computer network technology has been developing at a rapid speed. Many things that people thought were remote or even impossible 10 years ago can now become a reality, such as AR. AR is augmented reality. After simulating computer-generated text, image, 3D models, music, video, and other virtual information and applied to the real world, the two kinds of information complement each other, so as to achieve the “enhancement” of the real world. AR is augmented reality, a new field that is currently emerging. Using AR technology can add a realistic and virtual atmosphere to the real environment, giving users a better visual experience. Edge computing is a more efficient algorithm based on cloud computing. Traditional cloud computing, which requires all terminal devices to go from the cloud to the edge, consumes more energy and time, while using edge computing is more efficient. Applying edge computing to AR can render videos and pictures in real time, giving users a better experience. This paper is aimed at studying the ARToolKit target tracking and animation fusion technology based on edge computing and augmented reality and using this technology to propose and design an AR computing edge model. And compared with the traditional AR technology, the final experimental results show that the use of ARToolKit target tracking and animation fusion technology based on edge computing and augmented reality can reduce the minimum delay time by 1/3 compared with traditional cloud computing. The signal-to-noise ratio of the fused animation effect is significantly increased. Compared with the traditional gray value fusion, it only needs 1/2 of the number of calculations to achieve the same fusion effect.

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Section: Special Circumstancesmentioning

confidence: 99%

ARToolKit Target Tracking and Animation Fusion Technology Based on Edge Computing and Augmented Reality

Liu

Guo

2023

Wireless Communications and Mobile Computing

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“…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. Lei et al [21] analyze the spatial relationship between the target's location information and the swimming/drowning area of swimming pool to further determine the swimmer's drowning or swimming behaviour.…”

Section: Traditional Vision-based Methodsmentioning

confidence: 99%

“…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. Lei 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%

See 1 more Smart Citation

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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“…The basic idea is to use the relative position between adjacent pixels to classify pixels, to determine which line segments between pixels need to be filled [1]. Hou J proposed a filling algorithm based on chain code and combining the advantages of boundary point parity check and region growing method [2]. Volume graphics has been widely used in modeling, industrial design, and medical imaging.…”

Section: Introductionmentioning

confidence: 99%

Wireless Energy Transmission System for Capsule Endoscopy Based on Image Processing Technology

2022

FMSR

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The current capsule endoscopy power supply generally uses a battery, which has security risks and will affect the efficiency of capsule endoscopy to a large extent. Therefore, this paper constructs a wireless energy transmission system for capsule endoscopy based on high power and low loss transmission of image processing technology. By improving the merging mode of image processing, image processing is combined with wireless transmission to achieve efficient image processing in wireless transmission. Given the two-dimensional structure of the image, this paper also configures the block in the kernel as a two-dimensional structure. To obtain the best energy transfer performance, this paper tests all the possible combinations of the two dimensions. Then point-to-point high-frequency mode is used for inductive transmission of energy. Research shows that the wireless energy transmission system based on image processing technology can transmit energy in the frequency of 50 Hz-60 Hz, the loss rate is between 11% -19%, and the overall efficiency has small fluctuations in different modes. Compared with wired power transmission, this system has only a small loss increase, but its efficiency is more than 30% higher than that of battery. And after 100 groups of image testing, this system takes a short time and can meet the wireless power transmission effect of capsule endoscopy.

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RETRACTED: Swimming target detection and tracking technology in video image processing

Cited by 21 publications

References 6 publications

ARToolKit Target Tracking and Animation Fusion Technology Based on Edge Computing and Augmented Reality

ARToolKit Target Tracking and Animation Fusion Technology Based on Edge Computing and Augmented Reality

A video drowning detection device based on underwater computer vision

Wireless Energy Transmission System for Capsule Endoscopy Based on Image Processing Technology

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