Light-weight network for real-time adaptive stereo depth estimation

Video analysis of human motion has been widely used in intelligent monitoring, sports analysis, and virtual reality as a research hotspot in computer vision. It is necessary to decompose and track the movements in the process of movement in order to improve the training quality in dance training. The traditional motion tracking decomposition method, on the other hand, is unable to calculate the visual changes of adjacent key nodes, and the contour of 3D visual motion tracking remains ambiguous. This paper applies the human posture estimation algorithm in computer vision to the detection of key points of rectangular objects and obtains the heat map of key points of rectangular objects by adding a lightweight feature extraction network and a feature pyramid layer integrating multilayer semantic information, on the basis of summarizing and analyzing related research work at home and abroad. Because of the fusion of multilayer information, the network’s design not only reduces the amount of calculation and parameters but also improves the accuracy of the final detection result. The test results show that the proposed algorithm’s recognition accuracy has improved.

Wireless Communications and Mobile Computing

Section: Is a Classification Diagram Of Action Recognition Methodsmentioning

confidence: 99%

Real-Time Dance Posture Tracking Method Based on Lightweight Network

Wang

2022

“…In contrast to camouflaged objects, salient objects are the most noticeable objects in an image. The research of salient object detection (SOD) can promote image understanding [ 30 ], stereo matching [ 31 , 32 ], and medical disease detection [ 33 , 34 , 35 ]. In recent years, salient object detection based on deep learning has been improved by multi-scale feature fusion [ 36 ], attention mechanisms [ 37 ], and edge information [ 38 ].…”

Section: Related Researchmentioning

confidence: 99%

MAGNet: A Camouflaged Object Detection Network Simulating the Observation Effect of a Magnifier

Cai

et al. 2022

Entropy

In recent years, protecting important objects by simulating animal camouflage has been widely employed in many fields. Therefore, camouflaged object detection (COD) technology has emerged. COD is more difficult to achieve than traditional object detection techniques due to the high degree of fusion of objects camouflaged with the background. In this paper, we strive to more accurately and efficiently identify camouflaged objects. Inspired by the use of magnifiers to search for hidden objects in pictures, we propose a COD network that simulates the observation effect of a magnifier called the MAGnifier Network (MAGNet). Specifically, our MAGNet contains two parallel modules: the ergodic magnification module (EMM) and the attention focus module (AFM). The EMM is designed to mimic the process of a magnifier enlarging an image, and AFM is used to simulate the observation process in which human attention is highly focused on a particular region. The two sets of output camouflaged object maps were merged to simulate the observation of an object by a magnifier. In addition, a weighted key point area perception loss function, which is more applicable to COD, was designed based on two modules to give greater attention to the camouflaged object. Extensive experiments demonstrate that compared with 19 cutting-edge detection models, MAGNet can achieve the best comprehensive effect on eight evaluation metrics in the public COD dataset. Additionally, compared to other COD methods, MAGNet has lower computational complexity and faster segmentation. We also validated the model’s generalization ability on a military camouflaged object dataset constructed in-house. Finally, we experimentally explored some extended applications of COD.

“…In contrast to camouflaged objects, salient objects are the most noticeable objects in an image. The research of salient object detection can promote image understanding [33], stereo matching [34,35] and medical disease detection [36][37][38]. In recent years, salient object detection based on deep learning has been improved mainly by multiscale feature fusion [39], attention mechanisms [40] and edge information [41].…”

Section: Salient Object Detection Based On Deep Learningmentioning

confidence: 99%

MAGNet: A camouflaged object detection network simulating the observation effect of a magnifier

Cai

et al. 2022

Preprint

In recent years, protecting important objects by simulating animal camouflage has been widely used in many fields. Therefore, camouflaged object detection (COD) technology has emerged. COD is more difficult than traditional object detection techniques because of the high degree of fusion of camouflaged objects with the background. In this paper, we strive to more accurately identify camouflaged objects. Inspired by the use of magnifiers to search for hidden objects in pictures, we propose a COD network that simulates the observation effect of a magnifier, called the MAGnifier Network (MAGNet). Specifically, our MAGNet contains two parallel modules, i.e., the ergodic magnification module (EMM) and the attention focus module (AFM). The EMM is designed to mimic the process of a magnifier enlarging an image, and AFM is used to simulate the observation process in which human attention is highly focused on a region. The two sets of output camouflaged object maps are merged to simulate the observation of an object by a magnifier. In addition, a weighted key point area perception loss function, which is more applicable to COD, is designed based on two modules to give higher attention to the camouflaged object. Extensive experiments demonstrate that compared with 14 cutting-edge detection models, MAGNet can achieve the best comprehensive effect on eight evaluation metrics on the public COD dataset, and the segmentation accuracy is significantly improved. We also validate the models' generalization ability on a military camouflaged object dataset constructed in-house. Finally, we experimentally explore some extended applications of COD.