期刊:
Frontiers in Genetics,2021年12:763153 ISSN:1664-8021
通讯作者:
He, X.;Zhu, X.
作者机构:
College of Computer Science and Technology, Hengyang Normal University, China;Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, China;College of Computer, Xiangtan University, China;College of Computer Engineering and Applied Mathematics, Changsha University, China;The Social Sciences and Management University of Bamako, Mali
通讯机构:
[Zhu, X.; He, X.] C;College of Computer Science and Technology, China;College of Computer, China
关键词:
collaborative filtering model;data integration;essential proteins;PDI network;prediction model
摘要:
Video-based face detection and tracking technology has been widely used in video surveillance, safe driving, and medical diagnosis. In video sequences, most existing face detection and tracking methods face interference caused by occlusion, ambient illumination, and changes in human posture. To accurately track human faces in video sequences, we propose an efficient face detection and tracking framework based on deep learning, which includes a SENResNet face detection model and a Regression Network-based Face Tracking (RNFT) model. Firstly, the SENResNet model integrates the Squeeze and Excitation Network (SEN) with the Residual Neural Network (ResNet). To solve the problem that deep neural networks are difficult to train, we use ResNet to overcome the problem of gradient disappearance in deep network training. To fuse the features of each channel during the convolution operation, we further integrate the SEN module into the SENResNet model. SENResNet accurately detects facial information in each frame and extracts the position of the target face, thereby providing an initialization window for face tracking. Then, the RNFT model extracts facial features from adjacent frames and predict the position of the target face in the next frame. To address the problem of feature scaling, we add a correction network to the RNFT model. The improved RNFT model extracts the rectangular frame of the target face in the previous frame and strengthens the perception of feature scaling, thereby improving its accuracy. Extensive experimental results on public facial and video datasets show that the proposed SENResNet and RNFT models are superior to the state-of-the-art comparison methods in terms of accuracy and performance. (c) 2021 Elsevier Inc. All rights reserved.
作者:
Chenglong Wang;Tangle Peng;Longzhi Hu;Guanjun Liu
期刊:
Advances in Intelligent Systems and Computing,2021年 1274: 158-167 ISSN:2194-5357
通讯作者:
Wang, C.
作者机构:
[Peng T.; Liu G.; Wang C.] College of Computer Science and Technology, Hengyang Normal University, Hengyang, 421002, China;[Hu L.] Zhongxing Telecommunication Equipment Corporation, Shenzhen, 518000, China
通讯机构:
[Wang, C.] C;College of Computer Science and Technology, China
会议名称:
10th International Conference on Computer Engineering and Networks, CENet 2020
会议时间:
16 October 2020 through 18 October 2020
会议论文集名称:
The 10th International Conference on Computer Engineering and Networks
关键词:
CenSurE features;FREAK descriptor;Unmanned aerial vehicle scene matching algorithm
期刊:
Advances in Intelligent Systems and Computing,2021年1143:135-143 ISSN:2194-5357
通讯作者:
Chen, Z.
作者机构:
College of Computer Science and Technology, Hengyang Normal University, Hengyang, 421002, China;Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang, 421002, China;[Chen J.] Department of Computer Engineering, Ajou University, Suwon, 16499, South Korea;[Tian X.; Chen Z.; Lei T.] College of Computer Science and Technology, Hengyang Normal University, Hengyang, 421002, China, Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang, 421002, China
通讯机构:
[Chen, Z.] C;College of Computer Science and Technology, China
会议名称:
9th International Conference on Computer Engineering and Networks, CENet2019
会议时间:
18 October 2019 through 20 October 2019
会议论文集名称:
Proceedings of the 9th International Conference on Computer Engineering and Networks
作者机构:
College of Computer Science and Technology, Hengyang Normal University, Hengyang, 421002, China;Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang, 421002, China;[Tang S.; Jiang J.] College of Computer Science and Technology, Hengyang Normal University, Hengyang, 421002, China, Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang, 421002, China
会议名称:
10th International Conference on Computer Engineering and Networks, CENet 2020
作者机构:
[Zhu Y.] College of Computer Science and Technology, Hengyang Normal University, Hengyang, 421002, China;Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang, 421002, China;[Liu Q.; Zhang F.; Sun Y.; Wang Y.] College of Computer Science and Technology, Hengyang Normal University, Hengyang, 421002, China, Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang, 421002, China
会议名称:
10th International Conference on Computer Engineering and Networks, CENet 2020
作者机构:
[李浪; 冯景亚; 刘波涛; 郭影; 李秋萍] Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang Normal University, Hengyang;421002, China;College of Information Science and Engineering, Hunan Normal University, Changsha;410081, China;College of Computer Science and Technology, Hengyang Normal University, Hengyang
通讯机构:
[Jingya Feng] H;Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang Normal University, Hengyang, China<&wdkj&>College of Information Science and Engineering, Hunan Normal University, Changsha, China
摘要:
In this era of pervasive computing, low-resource devices have been deployed in various fields. PRINCE is a lightweight block cipher designed for low latency, and is suitable for pervasive computing applications. In this paper, we propose new circuit structures for PRINCE components by sharing and simplifying logic circuits, to achieve the goal of using a smaller number of logic gates to obtain the same result. Based on the new circuit structures of components and the best sharing among components, we propose three new hardware architectures for PRINCE. The architectures are simulated and synthesized on different programmable gate array devices. The results on Virtex-6 show that compared with existing architectures, the resource consumption of the unrolled, low-cost, and two-cycle architectures is reduced by 73, 119, and 380 slices, respectively. The low-cost architecture costs only 137 slices. The unrolled architecture costs 409 slices and has a throughput of 5.34 Gb/s. To our knowledge, for the hardware implementation of PRINCE, the new low-cost architecture sets new area records, and the new unrolled architecture sets new throughput records. Therefore, the newly proposed architectures are more resource-efficient and suitable for lightweight, latency-critical applications.
作者机构:
[Liang X.; Li Q.; Zhao J.; Zhang J.] College of Computer Science and Technology, Hengyang Normal University, Hengyang, 421002, China;Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang, 421002, China;[Li L.] College of Computer Science and Technology, Hengyang Normal University, Hengyang, 421002, China, Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang, 421002, China
会议名称:
9th International Conference on Computer Engineering and Networks, CENet2019