Human Motion Target Recognition Using Convolutional Neural Network and Global Constraint Block Matching

Liu, Wei; Li, Shangbin

doi:10.1109/access.2020.2986305

Cited by 8 publications

(21 citation statements)

References 47 publications

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“…e average similarity between the motion trajectory tracking results of the algorithm in literature [14] and the real trajectory is 46.0%, and the average similarity between the motion trajectory tracking results of the algorithm in literature [15] and the real trajectory is 54.6%. Compared with other algorithms, the average similarity between the motion trajectory tracking results of the proposed algorithm and the real trajectory is 95.1%, which indicates that the proposed algorithm has a better dynamic human motion tracking effect and verifies the effectiveness of the proposed algorithm for multimodal analysis.…”

Section: Dynamic Human Motion Tracking Effects Experimentmentioning

confidence: 94%

“…However, the integrity was poor. Literature [14] proposed a human motion target recognition algorithm based on CNN and global constraint block matching, which achieves human motion target recognition by matching block scores and spatial constraint weight calculation, but power consumption is high. In literature [15], a target tracking recognition system was developed using an intelligent framework system with accurate camera positioning, fast image processing, multimodal information fusion capabilities, and an optimized neural network-based target recognition algorithm with strong robustness, but the fineness is not enough.…”

Section: Related Workmentioning

confidence: 99%

“…Experiment with literature [11] algorithm, literature [12] algorithm, literature [13] algorithm, literature [14] algorithm, literature [15] algorithm, and the proposed algorithm to validate the application effect of different algorithms.…”

Section: Evaluation Criteriamentioning

confidence: 99%

“…According to Figure 5, the proposed algorithm and the other comparison algorithms are compared in different motion trajectories, and the results show that the motion trajectory tracking results of the proposed algorithm have a high overlap with the real trajectory, and it has superior performance in all four trajectories. In trajectory one, the overlap between the algorithm of literature [11], the algorithm of literature [12], the algorithm of literature [13], the algorithm of literature [14], and the algorithm of literature [15] and the actual path trajectory is low. In trajectory two, the literature [12] has a relatively high degree of overlap with the actual path trajectory but shows a large offset in the edge portion, while the rest of the remaining literature shows a much larger deviation.…”

Section: Dynamic Human Motion Tracking Effects Experimentmentioning

confidence: 99%

“…The proposed algorithm Literature [12]algorithm Literature [14]algorithm Literature [11]algorithm Literature [13]algorithm Literature [15]algorithm…”

Section: Actual Path Trajectorymentioning

confidence: 99%

See 4 more Smart Citations

Gesture Tracking and Recognition Algorithm for Dynamic Human Motion Using Multimodal Deep Learning

Xia

Xing

2022

Security and Communication Networks

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To address the problems of the traditional human motion gesture tracking and recognition methods, such as poor tracking effect, low recognition accuracy, high frame loss rate, and long-time cost, a dynamic human motion gesture tracking and recognition algorithm using multimode deep learning was proposed. Firstly, the collected human motion images are repaired in the three-dimensional (3D) environment, and the multimodal 3D human motion model is reconstructed using the processed images. Secondly, according to the results of model reconstruction, the camera gesture and other parameters of the keyframe are used to construct the target tracking optimization function so as to achieve the purpose of accurate tracking of human motion. Finally, for multimodal human motion gesture learning, a convolutional neural network (CNN) is developed. The trained CNN is utilized to complete dynamic human motion recognition after convolutional and pooling calculations. The results demonstrate that the proposed algorithm is effective in tracking human motion gestures. The average recognition accuracy is 96%, the average frame loss rate is 8.8%, the time cost is low, and the proposed algorithm has a high F-measure and much lower power consumption than other algorithms, indicating that the proposed algorithm is effective.

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Section: Dynamic Human Motion Tracking Effects Experimentmentioning

confidence: 94%

Section: Related Workmentioning

confidence: 99%

Section: Evaluation Criteriamentioning

confidence: 99%

Section: Dynamic Human Motion Tracking Effects Experimentmentioning

confidence: 99%

“…The proposed algorithm Literature [12]algorithm Literature [14]algorithm Literature [11]algorithm Literature [13]algorithm Literature [15]algorithm…”

Section: Actual Path Trajectorymentioning

confidence: 99%

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Gesture Tracking and Recognition Algorithm for Dynamic Human Motion Using Multimodal Deep Learning

Xia

Xing

2022

Security and Communication Networks

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Detection of Commodities Based on Multi-Feature Fusion and Attention Screening by Entropy Function Guidance

Xie,

Dong

et al. 2023

IEEE Access

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Although traditional convolutional neural networks (CNN) have been significantly improved for target detection, they cannot be completely applied to objects with occlusions in commodity detection. Therefore, we propose a target detection method based on an improved YOLOv5 model and an improved attention mechanism algorithm is proposed to solve the commodity occlusion problem. This method improves the traditional YOLO deep convolution network, features a more detailed BiFPN layer, and performs lightweight two-way feature fusion, where the multidimensional features of the commodities are convolved and fused, thus improving the overall detection speed and accuracy of the YOLO-R algorithm. Feature entropy is introduced to the attention channel to restrict the threshold value and obtain the global information of the occlusion target. The global information obtained is fused with a bidirectional feature pyramid layer to enhance the robustness of the features. This method could accurately and quickly detect the occluded commodities and the detection accuracy has been greatly improved. Experiments show that the improved YOLO-R model can improve the accuracy and speed of commodity detection, and can achieve good results in objective evaluation. The average accuracy of commodity detection on the selfmade product dataset is up to 97.80%, and the detection rate is 22.72F/s. Therefore, the method in this paper has high detection accuracy and fast detection speed.INDEX TERMS Lightweight feature fusion, attention channel, feature entropy, bidirectional feature.

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Mobile Big Data Analytics for Human Behavior Recognition in Wireless Sensor Network Based on Transfer Learning

Cui

Ren

2023

J. Inter. Net.

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Big data analysis of human behavior can provide the basis and support for the application of various scenarios. Using sensors for human behavior analysis is an effective means of identification method, which is very valuable for research. To address the problems of low recognition accuracy, low recognition efficiency of traditional human behavior recognition (HBR) algorithms in complex scenes, in this paper, we propose an HBR algorithm for Mobile Big data analytics in wireless sensor network using improved transfer learning. First, different wireless sensors are fused to obtain human behavior mobile big data, and then by analyzing the importance of human behavior features (HBF), the dynamic change parameters of HBF extraction threshold are calculated. Second, combined with the dynamic change parameters of threshold, the HBF of complex scenes are extracted. Finally, the best classification function of human behavior in complex scenes is obtained by using the classification function of HBF in complex scenes. Human behavior in complex scenes is classified according to the HBF in the feature set. The HBR algorithm is designed by using the improved transfer learning network to realize the recognition of human behavior in complex scenes. The results show that the proposed algorithm can accurately recognize up to 22 HBF points, and can control the HBR time within 2 s. The human behavior false recognition rate of miscellaneous scenes is less than 10%. The recognition speed is above 10/s, and the recall rate can reach more than 98%, which improves the HBR ability of complex scenes.

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Human Motion Target Recognition Using Convolutional Neural Network and Global Constraint Block Matching

Cited by 8 publications

References 47 publications

Gesture Tracking and Recognition Algorithm for Dynamic Human Motion Using Multimodal Deep Learning

Gesture Tracking and Recognition Algorithm for Dynamic Human Motion Using Multimodal Deep Learning

Detection of Commodities Based on Multi-Feature Fusion and Attention Screening by Entropy Function Guidance

Mobile Big Data Analytics for Human Behavior Recognition in Wireless Sensor Network Based on Transfer Learning

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