Neurocomputing for internet of things: Object recognition and detection strategy

Qureshi, Kashif Naseer; Kaiwartya, Omprakash; Jeon, Gwanggil; Piccialli, Francesco

doi:10.1016/j.neucom.2021.04.140

Cited by 10 publications

(5 citation statements)

References 34 publications

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“…If the color of the moving target is relatively rich and the pixel value change time is too short, it will cause the target to be mistaken for the background. At this time, it is necessary to The value is controlled within 5-20, and the P-value is positively correlated with the detection effect, but if If the value is too large, the response speed of the indicator will slow down [8,9]. After obtaining the stability through Equation 3.7, the judgment threshold Rth is obtained as:…”

Section: Modeling Methodsmentioning

confidence: 99%

Research on a Human Moving Object Detection Method Based on Gaussian Model and Deep Learning

Sun

2024

SCPE

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In order to understand human motion object detection methods, the author proposes a research on human motion object detection method based on Gaussian model. Firstly, traditional Gaussian models are unable to detect complex scenes or slow moving targets. Therefore, an improved Gaussian model based moving object detection algorithm is proposed. Secondly, multiple Gaussian models are used to represent the features of each pixel in the moving target image, and based on the matching of each pixel in the image with the Gaussian model, it is considered as a background point. Conversely, it is based on the principle of the foreground, and the Gaussian model is updated. Finally, by updating the foreground model and calculating short-term stability indicators, the detection effect of moving targets is improved. By determining the Gaussian distribution and pixel relationship, new parameters are set to construct the background model and eliminate the impact caused by sudden changes in lighting. The experimental analysis results show that this method can effectively detect and track moving targets, with good noise resistance, high clarity, and an accuracy rate of up to 99%. Compared with traditional Gaussian model methods, the improved method can more effectively detect moving targets and has better robustness.

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Section: Modeling Methodsmentioning

confidence: 99%

Research on a Human Moving Object Detection Method Based on Gaussian Model and Deep Learning

Sun

2024

SCPE

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“…As presented in equations ( 6), (7), where z indicates the actual audio. D z is the expectation operator applied to the original audio clips.…”

Section: Loss Functionmentioning

confidence: 99%

“…An encoded message can be disguised in a cover, such as a clip, image or audio recording [6]. Covert communication might be made possible through the stego, a covert data storage device [7]. A wide range of multimedia security situations, such as privacy protection, has been aided by steganography's use [8].…”

Section: Introductionmentioning

confidence: 99%

Identifying optimal message embedding location in audio steganography using generative adversarial networks

Hajer¹,

Anbar

2022

EEJET

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Audio steganography (AS) uses the auditory redundancy of the human ear to conceal the hidden message inside the audio track. In recent studies, deep learning-based steganalysis has swiftly revealed AS by extracting high-dimensional stego acoustic features for categorization. There is still an opportunity for improvement in the current audio steganography required for managing communication confidentiality, access control and data protection. The main objective of this research is to improving the data protection by identifying the data embedding location in the audio. Generative Adversarial Network-based Audio Steganography Framework (GAN-ASF) is presented in this study, and it can automatically learn to provide better cover audio for message embedding. The suggested framework's training architecture comprises a generator, a discriminator, and a steganalyzer learned using deep learning. The Least Significant Bit Matching (LSBM) message embedding technique encrypts the secret message into the steganographic cover audio, which is then forwarded to a trained steganalyzer for misinterpretation as cover audio. After performing the training, stenographic cover audio has been generated for encoding the secret message. Here, Markov model of co-frequency sub images to generate the best cover frequency sub-image to locate an image's hidden payload. Steganographic cover audio created by GAN-ASF has been tested and found to be of excellent quality for embedding messages. The suggested method's detection accuracy is lower than that of the most current state-of-the-art deep learning-based steganalysis. This payload placement approach has considerably increased stego locations' accuracy in low frequencies. The test results GAN-ASF achieves a performance ratio of 94.5 %, accuracy ratio of 96.2 %, an error rate of 15.7 %, SNR 24.3 %, and an efficiency ratio of 94.8 % compared to other methods.

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“…With the development of modern integration technologies and hardware devices, the recognition and detection of mobile and static objects have more and more application scenarios. In the literature [16], a convolutional neural network target image recognition algorithm combined with 5G technology is proposed for mobile objects in the Internet of Things, effectively improving the recognition accuracy of target detection. The literature [17] incorporated 3D convolutional layers into convolutional neural networks to solve the problem of recognizing moving objects by extracting spatio-temporal features, and the experimental results showed that the model helps extract more representative features.…”

Section: Introductionmentioning

confidence: 99%

Research on image recognition and processing of motion targets of warehouse logistics robots

Zhao,

Zhou,

Zhang

2023

Applied Mathematics and Nonlinear Sciences

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In developing robots for warehouse logistics, image recognition and processing for moving targets are the cornerstone of subsequent work. In this paper, the Meanshift algorithm is extended to continuous image sequences, and the Camshift algorithm for motion target tracking in a warehouse environment is proposed to obtain effective tracking of targets through the probability distribution when the color of continuous images changes dynamically. Based on target tracking, a feature-matching-based image recognition method is constructed. The scene image is first treated with improved Gamma correction for light equalization, and then image features are extracted using SURF feature points. Regarding running time, the feature matching method is, on average, 2.03 seconds faster than FLDA and 0.96 seconds faster than PCAFLDA under the same external conditions. By optimizing the computational structure, the feature-matching method can address the need for efficiency in warehouse logistics.

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Neurocomputing for internet of things: Object recognition and detection strategy

Cited by 10 publications

References 34 publications

Research on a Human Moving Object Detection Method Based on Gaussian Model and Deep Learning

Research on a Human Moving Object Detection Method Based on Gaussian Model and Deep Learning

Identifying optimal message embedding location in audio steganography using generative adversarial networks

Research on image recognition and processing of motion targets of warehouse logistics robots

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