Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition最新文献_第4页

Study on Variable Step Size Blind Equalization Algorithm Based on CMA 基于CMA的变步长盲均衡算法研究

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3573997

Mingyu Yang, Dongming Xu

引用次数: 0

SN-YOLO: Improved YOLOv5 with Softer-NMS and SIOU for Object Detection SN-YOLO:改进的YOLOv5与soft - nms和SIOU的目标检测

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3574029

Wanyu Deng, Zhen Wang

引用次数: 0

Joint Multi-Scale Residual and Motion Feature Learning for Action Recognition 联合多尺度残差和运动特征学习用于动作识别

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3574082

Linfeng Yang, Zhixiang Zhu, Chenwu Wang, Pei Wang, Shaobo Hei

{"title":"Joint Multi-Scale Residual and Motion Feature Learning for Action Recognition","authors":"Linfeng Yang, Zhixiang Zhu, Chenwu Wang, Pei Wang, Shaobo Hei","doi":"10.1145/3573942.3574082","DOIUrl":"https://doi.org/10.1145/3573942.3574082","url":null,"abstract":"For action recognition, two-stream networks consisting of RGB and optical flow has been widely used, showing high recognition accuracy. However, optical flow computation is time-consuming and requires a large amount of storage space, and the recognition efficiency is very low. To alleviate this problem, we propose an Adaptive Multi-Scale Residual (AMSR) module and a Long Short Term Motion Squeeze (LSMS) module, which are inserted into the 2D convolutional neural network to improve the accuracy of action recognition and achieve a balance of accuracy and speed. The AMSR module adaptively fuses multi-scale feature maps to fully utilize the semantic information provided by deep feature maps and the detailed information provided by shallow feature maps. The LSMS module is a learnable lightweight motion feature extractor for learning long-term motion features of adjacent and non-adjacent frames, thus replacing the traditional optical flow and improving the accuracy of action recognition. Experimental results on UCF-101 and HMDB-51 datasets demonstrate that the method proposed in this paper achieves competitive performance compared to state-of-the-art methods with only a small increase in parameters and computational cost.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134520985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Face Forgery Detection Method Based on Augmented Dual-Stream Networks 一种基于增强双流网络的人脸伪造检测方法

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3574030

Yumei Liu, Yong Zhang, Weiran Liu

{"title":"A Novel Face Forgery Detection Method Based on Augmented Dual-Stream Networks","authors":"Yumei Liu, Yong Zhang, Weiran Liu","doi":"10.1145/3573942.3574030","DOIUrl":"https://doi.org/10.1145/3573942.3574030","url":null,"abstract":"The current face forgery methods based on deep learning are becoming more mature and abundant, and existing detection techniques have some limitations and applicability issues that make it difficult to effectively detect such behaviour. In this paper, we propose an enhanced dual-stream FC_2_stream network model based on dual-stream networks to detect forged regions in manipulated face images through end-to-end training of the images. The RGB stream is used to extract features from the RGB image to find the forged traces; the noise stream uses the filtering layer of the SRM (Steganalysis Rich Model) model to extract the noise features and find the inconsistency between the noise in the real region and the forged region in the fake face, then the features of the two streams are fused with a bilinear pooling layer to predict the forged region, and finally the forged region is determined by whether the blending boundary of the forged image is displayed to determine the image authenticity. Experiments conducted on four benchmark datasets show that our model is still effective against forgeries generated by unknown face manipulation methods, and also demonstrate the superior generalisation capability of our model.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130377352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Spatiotemporal Attention Convolutional Neural Network for Video Crowd Counting 一种用于视频人群统计的时空注意卷积神经网络

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3574069

Shangjie Zhang, Yuelei Xiao

引用次数: 0

Routing Method Based on Connectivity and Latency in VANET VANET中基于连通性和时延的路由方法

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3573991

Hua Liu, Wujun Yang, Zhixian Chang, Min Shi

引用次数: 0

UAV Visual Tracking Algorithm Based on Feature Fusion of the Attention Mechanism 基于注意力机制特征融合的无人机视觉跟踪算法

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3574035

Sugang Ma, Zixian Zhang, Zhixian Zhao, Xiaobao Yang, Zhiqiang Hou

引用次数: 0

MEMS Gyroscope Temperature Compensation Based on SSA-RBF Neural Network 基于SSA-RBF神经网络的MEMS陀螺仪温度补偿

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3573959

Yuanhua Liu, Ziwei Wang, Xinliang Niu

{"title":"MEMS Gyroscope Temperature Compensation Based on SSA-RBF Neural Network","authors":"Yuanhua Liu, Ziwei Wang, Xinliang Niu","doi":"10.1145/3573942.3573959","DOIUrl":"https://doi.org/10.1145/3573942.3573959","url":null,"abstract":"The output of the Micro Electro-mechanical System (MEMS) gyroscope is susceptible affected by temperature drift, which reduces the measurement accuracy of the gyroscope. In this paper, a gyroscope temperature compensation method based on sparrow search algorithm (SSA) and radial basis function (RBF) neural network is proposed to reduce the temperature drift error of gyroscope. Firstly, we utilize the RBF neural network to establish the model of temperature error on the original output of gyroscope; then SSA is employed to find the optimal parameters of the RBF neural network in order to improve its search speed and generalization performance; finally, the optimized RBF neural network is applied to the temperature compensation of the gyroscope. The numerical simulation and comparison results under different temperatures demonstrate that, compared with polynomial and RBF neural network, the SSA-RBF neural network compensation method has superior compensation accuracy and faster convergence speed, which significantly reduces the maximum error, mean value and the standard deviation of gyroscope. Thus, the proposed SSA-RBF method can obtain more accurate fitting performance, effectively compensate the temperature error of MEMS gyroscope, and improve the MEMS gyroscope measurement accuracy.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125258745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research and Implementation of Multi-feature Tracking Algorithms 多特征跟踪算法的研究与实现

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3574040

Xinyue Zhang, Yao Tang

引用次数: 0

Improved atrial fibrillation recognition algorithm based on residual network 基于残差网络的改进房颤识别算法

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3574118

Zhiqiang Bao, Ting Ai, Ying Bai

{"title":"Improved atrial fibrillation recognition algorithm based on residual network","authors":"Zhiqiang Bao, Ting Ai, Ying Bai","doi":"10.1145/3573942.3574118","DOIUrl":"https://doi.org/10.1145/3573942.3574118","url":null,"abstract":"An improved residual network model is proposed to deal with the complex and changeable characteristics of one-dimensional electrocardiogram. In this model, firstly, in order to avoid the network degradation problem of the model along with the deepening of the number of layers, when extracting various deep-level features of ECG signals using multiple convolution layers in CNN, the residual module is integrated into the network, and an appropriate shortcut connection is selected to connect the input with the superposition output of the corresponding convolution layer to construct a deep residual network to extract more abstract signal features. Secondly, the output of the last residual module is sent to the GAP layer, and the parameters of this layer are greatly reduced compared with those of the full connection layer, which is equivalent to the compression of the model, and thus the over-fitting of the model is avoided to a certain extent. Finally, the original ECG signals were automatically classified based on the PCinCC2017 database to complete the recognition of atrial fibrillation. Experimental results show that the proposed algorithm has a classification accuracy of 86% and a F1 measure of 83%, which prove the feasibility of the model and the effectiveness of the algorithm.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129896713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0