Proceedings of the 2022 5th International Conference on Machine Vision and Applications最新文献_第2页

Path Planning for Autonomous Cars 自动驾驶汽车的路径规划

Proceedings of the 2022 5th International Conference on Machine Vision and Applications Pub Date : 2022-02-18 DOI: 10.1145/3523111.3523124

Zhou Wu

引用次数: 0

Mean-variance Based Risk-sensitive Reinforcement Learning with Interpretable Attention 基于均值方差的可解释注意风险敏感强化学习

Proceedings of the 2022 5th International Conference on Machine Vision and Applications Pub Date : 2022-02-18 DOI: 10.1145/3523111.3523127

Woo Kyung Kim, Youngseok Lee, Hong-Suh Woo

引用次数: 2

Optimizing Train-Test Data for Person Re-Identification in Real-World Applications 优化列车测试数据在现实世界中的再识别应用

Proceedings of the 2022 5th International Conference on Machine Vision and Applications Pub Date : 2022-02-18 DOI: 10.1145/3523111.3523121

Herman G. J. Groot, Tunç Alkanat, E. Bondarev, P. D. With

{"title":"Optimizing Train-Test Data for Person Re-Identification in Real-World Applications","authors":"Herman G. J. Groot, Tunç Alkanat, E. Bondarev, P. D. With","doi":"10.1145/3523111.3523121","DOIUrl":"https://doi.org/10.1145/3523111.3523121","url":null,"abstract":"Person re-identification (re-ID) aims to recognize an identity in non-overlapping camera views. Recently, re-ID received increased attention due to the growth of deep learning and its prominent applications in the field of automated video surveillance. The performance of deep learning-based methods relies heavily on the quality of training datasets and protocols. Particularly, parameters associated to the train and test set construction affect the overall performance. However, public re-ID datasets usually come with a fixed set of parameters, which are partly suitable for optimizing re-ID applications. In this paper, we study dataset construction parameters to improve re-ID performance. To this end, we first experiment on the temporal subsampling rate of the sequence of bounding boxes. Second, an experiment is performed on the effects of bounding-box enlargement under various temporal sampling rates. Thirdly, we analyze how the optimal choice of such dataset design parameters change with the dataset size. The experiments reveal that a performance increase of 2.1% Rank-1 is possible over a state-of-the-art re-ID model when optimizing the dataset construction parameters, thereby increasing the state-of-the-art performance from 91.9% to 94.0% Rank-1 on the popular DukeMTMC-reID dataset. The obtained results are not specific for the applied model and likely generalize to others.","PeriodicalId":185161,"journal":{"name":"Proceedings of the 2022 5th International Conference on Machine Vision and Applications","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124612572","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 Semantic Segmentation Approach for Road Defect Detection and Quantification 一种基于语义分割的道路缺陷检测与量化方法

Proceedings of the 2022 5th International Conference on Machine Vision and Applications Pub Date : 2022-02-18 DOI: 10.1145/3523111.3523113

Deepak Nagaraj, Marcel Mutz, Nisha George, Prateek Bansal, Dirk Werth

引用次数: 1

Research on Human Motion Recognition Based on Computer Vision Technology 基于计算机视觉技术的人体运动识别研究

Proceedings of the 2022 5th International Conference on Machine Vision and Applications Pub Date : 2022-02-18 DOI: 10.1145/3523111.3523123

Jie Sun

引用次数: 1

Modeling and Prediction of Invasive Systolic Blood Pressure after General Anesthesia Based on Fusion Algorithm 基于融合算法的全身麻醉后有创收缩压建模与预测

Proceedings of the 2022 5th International Conference on Machine Vision and Applications Pub Date : 2022-02-18 DOI: 10.1145/3523111.3523129

Ziyi Chen, Lei Zhang, Qianling Wang

{"title":"Modeling and Prediction of Invasive Systolic Blood Pressure after General Anesthesia Based on Fusion Algorithm","authors":"Ziyi Chen, Lei Zhang, Qianling Wang","doi":"10.1145/3523111.3523129","DOIUrl":"https://doi.org/10.1145/3523111.3523129","url":null,"abstract":"During surgery, invasive systolic blood pressure is an important basis for doctors to judge the patient's life state, which will directly affect the security of the surgery. Accurately predict the changes of invasive systolic blood pressure during general anesthesia help to reduce the risk of surgery. In order to cope with the increasing surgical risk by fluctuations of invasive systolic blood pressure, this paper optimized and combined the traditional machine learning algorithm, and put forward a new fusion algorithm to predict the invasive systolic blood pressure after general anesthesia. In the modeling process, the patients’ basic physical conditions, disease status, and intraoperative data collected by monitoring instrument during the surgical preparation stage were used as characteristic variable. In this paper, Linear Regression, Support Vector Machine Regression, Lasso Regression, and Ridge Regression were used to establish the new fusion algorithm. When the absolute error within 15mmHg, the fusion algorithm's predicting accuracy of invasive systolic blood pressure after general anesthesia reached 91.5%. The accurate prediction of invasive systolic blood pressure after general anesthesia in the preparation stage provides sufficient time for doctors to respond and reduces the risk of surgery to a certain extent.","PeriodicalId":185161,"journal":{"name":"Proceedings of the 2022 5th International Conference on Machine Vision and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128475830","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

Iterative Pruning-based Model Compression for Pose Estimation on Resource-constrained Devices 基于迭代剪枝的资源受限设备姿态估计模型压缩

Proceedings of the 2022 5th International Conference on Machine Vision and Applications Pub Date : 2022-02-18 DOI: 10.1145/3523111.3523128

Sung Hyun Choi, Wonje Choi, Youngseok Lee, Honguk Woo

引用次数: 0

Proceedings of the 2022 5th International Conference on Machine Vision and Applications 2022年第五届机器视觉与应用国际会议论文集

Proceedings of the 2022 5th International Conference on Machine Vision and Applications Pub Date : 1900-01-01 DOI: 10.1145/3523111

引用次数: 0