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Proceedings of the 2022 International Conference on Pattern Recognition and Intelligent Systems最新文献

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Animaton: Scriptable Finite Automaton for Animation Design in Unity3D Game Engine 动画:在Unity3D游戏引擎中用于动画设计的脚本有限自动机
Proceedings of the 2022 International Conference on Pattern Recognition and Intelligent Systems Pub Date : 2022-07-29 DOI: 10.1145/3549179.3549196
Wanwan Li
{"title":"Animaton: Scriptable Finite Automaton for Animation Design in Unity3D Game Engine","authors":"Wanwan Li","doi":"10.1145/3549179.3549196","DOIUrl":"https://doi.org/10.1145/3549179.3549196","url":null,"abstract":"In this paper, we propose a novel technical approach for animation design in the Unity3D game engine using the scriptable finite automaton called Animaton that specifies the transitions between positions, rotations, and animation clips for each animated virtual character. In our animation design interface, the animation designer can write easy-to-use C-like scripts to define the animation logic, drag and drop each state label in the virtual scene, and then can use different input strings to simulate different animation sequences. We conduct a series of numerical experiments to demonstrate how is our proposed approach applied to the animation design process and how potentially it can improve the animation design efficiency.","PeriodicalId":105724,"journal":{"name":"Proceedings of the 2022 International Conference on Pattern Recognition and Intelligent Systems","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124705048","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: 2
An LSTM-based Method for Recognition and Prediction of Aircraft Formation 基于lstm的飞机编队识别与预测方法
Proceedings of the 2022 International Conference on Pattern Recognition and Intelligent Systems Pub Date : 2022-07-29 DOI: 10.1145/3549179.3549193
Futai Liang, Yan Zhou, Zheng Zhang, Xin Chen, Xiaojie Tang, Qiang Sun
{"title":"An LSTM-based Method for Recognition and Prediction of Aircraft Formation","authors":"Futai Liang, Yan Zhou, Zheng Zhang, Xin Chen, Xiaojie Tang, Qiang Sun","doi":"10.1145/3549179.3549193","DOIUrl":"https://doi.org/10.1145/3549179.3549193","url":null,"abstract":"Aircraft formation recognition and prediction are of great significance in modern air combat. Aiming at the problems of many manual interventions and complex implementation through traditional aircraft formation recognition methods, an intelligent recognition and prediction method of aircraft formation is proposed. First, a formation coding method is designed, which is combined with Support Vector Machine (SVM) to construct a formation recognition model. Then, a formation prediction model is constructed based on the Long-Short-Term Memory network (LSTM) and the recognition model. Finally, a dataset is generated to train the two models, and the trained model can be used for formation recognition and prediction. After experimental verification, the method proposed in the paper has better recognition and prediction effects on formations, the recognition accuracy can reach 95.5%, and the accuracy of formation prediction can reach 95%.","PeriodicalId":105724,"journal":{"name":"Proceedings of the 2022 International Conference on Pattern Recognition and Intelligent Systems","volume":"606 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116388808","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
Air Target Route Rule Mining Based on Clustering 基于聚类的空中目标航路规则挖掘
Proceedings of the 2022 International Conference on Pattern Recognition and Intelligent Systems Pub Date : 2022-07-29 DOI: 10.1145/3549179.3549195
Chenhao Zhang, Yan Zhou, Jing Wang, Zihao Song
{"title":"Air Target Route Rule Mining Based on Clustering","authors":"Chenhao Zhang, Yan Zhou, Jing Wang, Zihao Song","doi":"10.1145/3549179.3549195","DOIUrl":"https://doi.org/10.1145/3549179.3549195","url":null,"abstract":"The air target activity is frequent, how to better study and judge the target is an important problem facing at present. The route rule of target is an important feature of judge and recognition. In order to improve the intelligence degree of air target routing rule mining and get rid of the interference of artificial experience and subjective factors, this paper proposes a method of air target route rule mining based on clustering. Firstly, the distance between any two routes is defined based on Hausdorff distance. Secondly, the K-means algorithm was improved, a distance threshold was set to ensure that the clustering centers would not be completely randomly selected, and the optimal number of clustering was selected by elbow method. Finally, routes in a certain airspace were obtained by simulation, and the rules of routes were obtained by clustering mining. Through the simulation experiment, 21 airlines were simulated. The optimal clustering number was determined to be 4 by elbow method, and the SSE of clustering by K-means algorithm was 1.03.","PeriodicalId":105724,"journal":{"name":"Proceedings of the 2022 International Conference on Pattern Recognition and Intelligent Systems","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122310039","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
Weakly Supervised Road Garbage Quantification System Based on WCCA 基于WCCA的弱监督道路垃圾量化系统
Proceedings of the 2022 International Conference on Pattern Recognition and Intelligent Systems Pub Date : 2022-07-29 DOI: 10.1145/3549179.3549187
Rongbo Fan, Xiaobo Fan, Hongliang Sun, Jun Chen, Jianhua Yang
{"title":"Weakly Supervised Road Garbage Quantification System Based on WCCA","authors":"Rongbo Fan, Xiaobo Fan, Hongliang Sun, Jun Chen, Jianhua Yang","doi":"10.1145/3549179.3549187","DOIUrl":"https://doi.org/10.1145/3549179.3549187","url":null,"abstract":"Mechanized road sweeping vehicles are an indispensable part of urban infrastructure. However, while improving work efficiency, the high energy consumption and noise pollution of their high-power dust collectors have become new problems that need to be solved. To achieve adoptive control of dust collection power, we propose a vision-based road sweeper intelligent power control system. The system use the proposed Weight Criss-Cross Attention (WCCA) module, embed into the Mobile V2 light-weight image-level classification network, to achieve weakly supervised pixel-by-pixel segmentation of road garbage area, and finally get the road garbage quantification result. With the proposed prior loss function based on the distribution of road garbage image data, WCCA can guide the convergence direction of the model to the correct target area. Finally, two state-of -the-art comparison algorithms are used to prove the superiority of the proposed algorithm.","PeriodicalId":105724,"journal":{"name":"Proceedings of the 2022 International Conference on Pattern Recognition and Intelligent Systems","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121028810","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
FPGA hardware implementation of Q-learning algorithm with low resource consumption FPGA硬件实现q -学习算法的低资源消耗
Proceedings of the 2022 International Conference on Pattern Recognition and Intelligent Systems Pub Date : 2022-07-29 DOI: 10.1145/3549179.3549181
Xiaojuan Liu, Jietao Diao, Nan Li
{"title":"FPGA hardware implementation of Q-learning algorithm with low resource consumption","authors":"Xiaojuan Liu, Jietao Diao, Nan Li","doi":"10.1145/3549179.3549181","DOIUrl":"https://doi.org/10.1145/3549179.3549181","url":null,"abstract":"Q-learning is a kind of reinforcement learning, having a wide range of applications varying in different fields. However, in some circumstances like robot control which has shorter training time requirement, Q-learning algorithm implemented on GPU or CPU may not meet the requirement. In this paper, we proposed a novel serial acceleration architecture for Q-learning algorithm and implemented the architecture on xczu7ev-ffvc1156 FPGA using Vivado 2019.1 development environment. As a result, the resource consumption is reduced by about 50% compared with the architecture proposed in [1],and the update cycle of Q-learning algorithm is fixed to 4 clock cycles.","PeriodicalId":105724,"journal":{"name":"Proceedings of the 2022 International Conference on Pattern Recognition and Intelligent Systems","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127655639","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 Machine-Learning Pipeline for Semantic-Aware and Contexts-Rich Video Description Method 基于语义感知和上下文丰富的视频描述方法的机器学习管道
Proceedings of the 2022 International Conference on Pattern Recognition and Intelligent Systems Pub Date : 2022-07-29 DOI: 10.1145/3549179.3549182
Yichiet Aun, Y. Khaw, Ming-Lee Gan, Ley-Ter Tin
{"title":"A Machine-Learning Pipeline for Semantic-Aware and Contexts-Rich Video Description Method","authors":"Yichiet Aun, Y. Khaw, Ming-Lee Gan, Ley-Ter Tin","doi":"10.1145/3549179.3549182","DOIUrl":"https://doi.org/10.1145/3549179.3549182","url":null,"abstract":"Video description (VD) methods use machine learning to automatically generate sentences to describe video contents. Global-description based VD (gVD) methods generates global description to provide the big picture of video scenes but they lack finer grain entities information. Meanwhile, modern entity-based VD (eVD) use deep learning to train ML models like object model (YOLOv3), human activity model (CNN), location tracking (DeepSORT) to resolve individual entity that made up the complete sentences. However, existing eVD are limited in the types of supported entities; thus, resulting in eVD generating sentences that contexts-deprived and incomplete to clearly describe video scenes. In addition, the entities resolved by eVD are isolated since they are inferred from different ML models; resulting in sentences that are not semantically cohesive; contextually and grammatically. In this paper, a two-stages ML pipeline (teVD) is proposed for a holistic and semantic-aware VD sentence generation. Firstly, a ML pipeline is designed to aggregate several high performing ML models for resolving fine grain entities to improve the accuracy of resolved entities. Second, the components in the entities set are ‘stitched’ together using an entity trimming method to (1) remove shadow entities and (2) to re-arrange entities based on linguistic rules to generate video descriptions that are context-aware and less ambiguous. The experimental results showed that teVD successfully improved the quality of generated sentences in short videos; achieving BLEU score of 48.01 and METEOR score of 32.80 on MSVD dataset.","PeriodicalId":105724,"journal":{"name":"Proceedings of the 2022 International Conference on Pattern Recognition and Intelligent Systems","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126522691","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
Multi-Scale and Kernel-Predicting Convolutional Networks for Monte Carlo Denoising 蒙特卡罗去噪的多尺度和核预测卷积网络
Proceedings of the 2022 International Conference on Pattern Recognition and Intelligent Systems Pub Date : 2022-07-29 DOI: 10.1145/3549179.3549183
Tianhan Gao, Yanjing Ge
{"title":"Multi-Scale and Kernel-Predicting Convolutional Networks for Monte Carlo Denoising","authors":"Tianhan Gao, Yanjing Ge","doi":"10.1145/3549179.3549183","DOIUrl":"https://doi.org/10.1145/3549179.3549183","url":null,"abstract":"Monte Carlo rendering has been widely used in many fields, such as movies, which pursue the photorealistic rendering effect. Monte Carlo rendering needs high sampling rates to get an accurate rendering effect, but the calculation cost is expensive. To keep costs down, one solution is to reduce the noise of the rendered image at reduced sampling rates. Because the traditional denoising method is based on higher and higher order regression models, it is prone to overfitting noise in the input. The Monte Carlo denoising method based on deep learning shows a certain denoising value. In this paper, we propose a kernel-predicting convolutional network with a multi-scale residual structure. Compared with previous methods, our method can extract features and perform residual learning at different scales, which can further remove low-frequency noise and improve the denoising quality.","PeriodicalId":105724,"journal":{"name":"Proceedings of the 2022 International Conference on Pattern Recognition and Intelligent Systems","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126543573","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
The Object Detection of Underwater Garbage with an Improved YOLOv5 Algorithm 基于改进YOLOv5算法的水下垃圾目标检测
Proceedings of the 2022 International Conference on Pattern Recognition and Intelligent Systems Pub Date : 2022-07-29 DOI: 10.1145/3549179.3549189
Xiao Teng, Yuhuan Fei, Kai He, Lihui Lu
{"title":"The Object Detection of Underwater Garbage with an Improved YOLOv5 Algorithm","authors":"Xiao Teng, Yuhuan Fei, Kai He, Lihui Lu","doi":"10.1145/3549179.3549189","DOIUrl":"https://doi.org/10.1145/3549179.3549189","url":null,"abstract":"Litter deposition in aquatic environments has devastating effects on marine ecological environment and poses a threat to a sustainable economy. Autonomous Underwater Vehicles (AUV) could solve the issue nicely by detecting and clearing litter. A good object detection algorithm is very important in the process of AUV detection and garbage collection. In this research, YOLOv5 was applied as the detection algorithm of the detector and the prediction side of the algorithm was improved. The anchor boxes of the model are re-clustered by using the improved KMeans++ algorithm, the loss function was optimized and the box loss function of the original model was replaced by CIoU. When detecting the trash_ICRA19 dataset, the results demonstrated that the improved model achieved a detection accuracy of 88.7%, a mean average precision (mAP) of 90.6%. The mean average precision of the research work was 9.6% higher than previous studies. The results showed that the improved model could realize the detection and identification of plastic waste in water.","PeriodicalId":105724,"journal":{"name":"Proceedings of the 2022 International Conference on Pattern Recognition and Intelligent Systems","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134283277","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: 1
Biparted Hyperboloid and Sphere Intersection Algorithm 双曲面与球的双向交算法
Proceedings of the 2022 International Conference on Pattern Recognition and Intelligent Systems Pub Date : 2022-07-29 DOI: 10.1145/3549179.3549185
Jing Zhu, Houjun Hang, Yong Cheng, Ping Guo, Yaling Wu, Yongyan Sun
{"title":"Biparted Hyperboloid and Sphere Intersection Algorithm","authors":"Jing Zhu, Houjun Hang, Yong Cheng, Ping Guo, Yaling Wu, Yongyan Sun","doi":"10.1145/3549179.3549185","DOIUrl":"https://doi.org/10.1145/3549179.3549185","url":null,"abstract":"Abstract. The focus of this paper is about biparted hyperboloid and sphere intersection algorithm. By coordinate transformation, the generalized cylindrical parametric equation of the biparted hyperboloid is first gained. Then a quartic equation with one unknown number is built. According to the distribution of the equation roots, the topological structure of the intersection curves can be judged accurately. In each valid subinterval, the parametric equations of intersection curves are foundwhich the intersection curves can be drawn correctly. Finally, some examples are provided to demonstrate the algorithm.The algorithm is suitable for the engineering application.","PeriodicalId":105724,"journal":{"name":"Proceedings of the 2022 International Conference on Pattern Recognition and Intelligent Systems","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133509437","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
The Research and Implementation of Clothing Style Transfer Algorithm Based on CycleGAN 基于CycleGAN的服装风格传递算法的研究与实现
Proceedings of the 2022 International Conference on Pattern Recognition and Intelligent Systems Pub Date : 2022-07-29 DOI: 10.1145/3549179.3549191
Yutong Wang, Luying Li
{"title":"The Research and Implementation of Clothing Style Transfer Algorithm Based on CycleGAN","authors":"Yutong Wang, Luying Li","doi":"10.1145/3549179.3549191","DOIUrl":"https://doi.org/10.1145/3549179.3549191","url":null,"abstract":"The rapid development of artificial intelligence has brought about changes in many industries, and the application of deep learning technology in apparel design has become a current research hotspot. Since human subjective consciousness plays a dominant role in design style during the design process, artificial intelligence methods can effectively avoid the problem. In this paper, we focus on the application of Cycle Generative Adversarial Network (CycleGAN) in clothing style migration by giving an overview of CycleGAN. To address the problems exhibited by traditional generative adversarial networks in clothing style migration, this paper adds a filtering link before model training, which makes the generative adversarial network more focused and the edges more clear in the process of style migration. Through the comparison of experimental results, it is verified that the method works better in clothing style migration.","PeriodicalId":105724,"journal":{"name":"Proceedings of the 2022 International Conference on Pattern Recognition and Intelligent Systems","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124603819","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
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