Infrared small moving target detection algorithm based on joint spatio-temporal sparse recovery

Li, Zhengzhou; Hou, Qian; Fu, Hongyi; Dai, Zhijiang; Yang, Lijiao; Jin, Gang; Li, Ruzhang

doi:10.1016/j.infrared.2015.01.008

Cited by 35 publications

(2 citation statements)

References 17 publications

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“…Based on BP neural network and adaptive Gaussian mixture model, a moving target detection algorithm is proposed in the literature, which can denoise the foreground image and is suitable for coarse segmentation of targets. This method can effectively denoise and keep the connection in the foreground [14].…”

Section: Related Workmentioning

confidence: 99%

Application of artificial intelligence wearable devices based on neural network algorithm in mass sports activity evaluation

Liang

2023

Preprint

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Based on the rapid development of big data, cloud computing, Internet of things and other technologies in recent years, intelligent hardware devices has been applied to all aspects of life. Under this background, some scholars have put forward relevant concepts such as "Smart Life". In the field of mass sports life, through the development and application of science and technology, there has been new changes related to the application of neural network algorithm technology and intelligent hardware devices. Therefore, artificial intelligence wearable devices based on wearable technology came into being. This paper analyzes the application of this device in mass sports activities. Then, this paper describes the key research technologies of motion data processing based on neural network algorithm, including: depth frame differential convolution neural network structure, motion data extraction method, human motion signal processing algorithm, etc; Then it analyzes the action recognition and interaction system design based on Intelligent wearable devices. Finally, it analyzes the recognition results of human action system, the accuracy of human action recognition system and the factors that affect the performance of the recognition system. It is concluded that the artificial intelligent wearable devices designed in this paper can be well used in popular sports activities. Finally, it introduces the research on the evaluation strategy of popular sports activities based on artificial intelligence, and hopes that this equipment can help public sports activities. This paper studies the neural network algorithm and applies it to the design process of artificial intelligence wearable devices, which promotes the development of mass sports activity evaluation.

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Section: Related Workmentioning

confidence: 99%

Application of artificial intelligence wearable devices based on neural network algorithm in mass sports activity evaluation

Liang

2023

Preprint

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“…TBD methods utilize the inter-frame information and accumulate signal energy of the target along the trajectory to detect targets, so it is suitable for the targets with the consistent trajectory. Some classical methods are three dimensional (3D) matched filters [9] and some other spatial-temporal methods [10]- [12] have been proposed recently. DBT methods can utilize the continuity of the target trajectory to select the true target in the detection result obtained based on the single frame detection method, including (HVS)-based methods [13]- [15] and the classification-based methods [16]- [18].…”

Section: A Related Workmentioning

confidence: 99%

Infrared Small Target Detection via Spatial-Temporal Total Variation Regularization and Weighted Tensor Nuclear Norm

et al. 2019

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The infrared small and dim targets are often buried in strong clutters and noise, which requires robust and efficient detection approaches to achieve search and track task. In this paper, a novel infrared small target detection approach based on tensor robust principal component analysis (RPCA) is proposed by fully utilizing both spatial and temporal information. Traditional total variation (TV) regularizationbased method only considers the spatial information of a single frame, and its efficiency cannot meet the real-time processing requirement. We incorporate an anisotropic spatial-temporal TV regularization to utilize both single-frame and inter-frame information. First, the input image sequence is transformed into an infrared patch tensor (IPT) model. Then, the spatial-temporal TV regularization and weighted IPT model (STTVWNIPT) is proposed to separate the target and background. For the low-rank background component, we adopt the weighted tensor nuclear norm and spatial-temporal TV regularization to describe the smoothness. For sparse target component and a noise component, we use the l 1 norm and Frobenius norm terms to characterize, respectively. Finally, the proposed model can be solved efficiently by the alternating direction method of multipliers (ADMM). The extensive experiments demonstrate that the proposed model outperforms the other competitive methods, and it is much more efficient than traditional TV-based method.INDEX TERMS Infrared patch-tensor model, small target detection, weighted tensor robust principal component analysis, total variation (TV).

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Application of artificial intelligence wearable devices based on neural network algorithm in mass sports activity evaluation

Liang

2023

Soft Comput

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Infrared small moving target detection algorithm based on joint spatio-temporal sparse recovery

Cited by 35 publications

References 17 publications

Application of artificial intelligence wearable devices based on neural network algorithm in mass sports activity evaluation

Application of artificial intelligence wearable devices based on neural network algorithm in mass sports activity evaluation

Infrared Small Target Detection via Spatial-Temporal Total Variation Regularization and Weighted Tensor Nuclear Norm

Application of artificial intelligence wearable devices based on neural network algorithm in mass sports activity evaluation

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