2023 20th Conference on Robots and Vision (CRV)最新文献

Sponsors: CRV 2023 赞助商:CRV 2023

2023 20th Conference on Robots and Vision (CRV) Pub Date : 2023-06-01 DOI: 10.1109/crv60082.2023.00008

引用次数: 0

Deformation Modeling for the Robotic Manipulation of 3D Elastic Objects using Physics-Informed Graph Neural Networks 基于物理信息图神经网络的三维弹性物体机器人操作的变形建模

2023 20th Conference on Robots and Vision (CRV) Pub Date : 2023-06-01 DOI: 10.1109/CRV60082.2023.00032

Angel J. Valencia, P. Payeur

引用次数: 0

2023 20th Conference on Robots and Vision (CRV) 2023第20届机器人与视觉会议(CRV)

2023 20th Conference on Robots and Vision (CRV) Pub Date : 2023-06-01 DOI: 10.1109/crv60082.2023.00002

引用次数: 0

Robust Scuba Diver Tracking and Recovery in Open Water Using YOLOv7, SORT, and Spiral Search 稳健的潜水员跟踪和恢复在开放水域使用YOLOv7, SORT，和螺旋搜索

2023 20th Conference on Robots and Vision (CRV) Pub Date : 2023-06-01 DOI: 10.1109/CRV60082.2023.00037

F. Lotfi, K. Virji, G. Dudek

{"title":"Robust Scuba Diver Tracking and Recovery in Open Water Using YOLOv7, SORT, and Spiral Search","authors":"F. Lotfi, K. Virji, G. Dudek","doi":"10.1109/CRV60082.2023.00037","DOIUrl":"https://doi.org/10.1109/CRV60082.2023.00037","url":null,"abstract":"Target tracking is a classic problem in computer vision, with numerous applications in robotics. However, tracking targets underwater presents additional complications due to the six degrees of freedom nature of the problem and the challenging visual environment. In this paper, we address the problem of robotic underwater tracking of scuba divers by partitioning it into two parts: vision and control. We propose a new approach that exploits a highly-maneuverable underwater robot to perform experiments in open water, coupling sensing and control for improved performance. To evaluate the temporal stability of different tracking paradigms, we introduce a new metric, frame-to-frame vari-ance, which is better suited to assess the smoothness of detections from the vision side. We implement PID controllers for control and a spiral search algorithm for target recovery in case of a tracking failure. Our approach only uses observations in the image plane, eliminating the need for robot localization or camera calibration. Using a tracking-by-detection paradigm that combines YOLOv7 for target detection, a tuned filtering technique for temporal stability, and a spiral search algorithm for target recovery, we demonstrate promising performance for long-term tracking. We evaluate our proposed paradigm on the VDD-C dataset and deploy it on an underwater robot for several experiments in open water. Our outcomes show consistency with the ones in the initial studies, and the spiral search algorithm demonstrates promising performance for recapturing a target after a tracking failure. Our approach delivers promising performance for robust underwater tracking, achieving successful open-water tracking scenarios in the presence of strong water currents.","PeriodicalId":325041,"journal":{"name":"2023 20th Conference on Robots and Vision (CRV)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124860449","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

Adaptive Multiple Distributed Bidirectional Spiral Path Planning for Foraging Robot Swarms 觅食机器人群的自适应多分布双向螺旋路径规划

2023 20th Conference on Robots and Vision (CRV) Pub Date : 2023-06-01 DOI: 10.1109/CRV60082.2023.00036

Qi Lu, Ryan Luna

{"title":"Adaptive Multiple Distributed Bidirectional Spiral Path Planning for Foraging Robot Swarms","authors":"Qi Lu, Ryan Luna","doi":"10.1109/CRV60082.2023.00036","DOIUrl":"https://doi.org/10.1109/CRV60082.2023.00036","url":null,"abstract":"The Distributed Deterministic Spiral Algorithm (DDSA) has shown great foraging efficiency in robot swarms. However, when the number of robots in the swarm increases, scalability becomes a significant bottleneck due to increased collisions among robots, making it challenging to deploy them in the search space (e.g., 20 robots). To address this issue, we propose an adaptive Multiple-Distributed Bidirectional Spiral Algorithm (MDBSA) that enhances scalability. Our proposed algorithm partitions the squared search arena into multiple identical squared regions and assigns robots to regions dynamically based on the number of regions. In each region, a bidirectional spiral search path is planned, and when a robot completes its search, it is assigned to either an unassigned region or a region with one robot. The two robots will then travel along the path from the starting and ending points of the spiral path. We evaluated the performance of robot swarms using the MDBSA algorithm in the ARGoS robot simulator. Our experimental results show that the proposed MDBSA algorithm outperforms DDSA. When robots deliver collected resources to regions instead of the center, it reduces collisions and significantly improves the scalability of the robot swarm. Our findings suggest that a multiple-distributed search strategy is an efficient solution for foraging robot swarms.","PeriodicalId":325041,"journal":{"name":"2023 20th Conference on Robots and Vision (CRV)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126872711","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

Learning-to-Count by Learning-to-Rank 通过学习排名来学习计数

2023 20th Conference on Robots and Vision (CRV) Pub Date : 2023-06-01 DOI: 10.1109/CRV60082.2023.00021

Adrian d'Alessandro, Ali Mahdavi-Amiri, G. Hamarneh

引用次数: 1

Class Instance Balanced Learning for Long-Tailed Classification 长尾分类的类实例平衡学习

2023 20th Conference on Robots and Vision (CRV) Pub Date : 2023-06-01 DOI: 10.1109/CRV60082.2023.00023

Marc-Antoine Lavoie, Steven L. Waslander

{"title":"Class Instance Balanced Learning for Long-Tailed Classification","authors":"Marc-Antoine Lavoie, Steven L. Waslander","doi":"10.1109/CRV60082.2023.00023","DOIUrl":"https://doi.org/10.1109/CRV60082.2023.00023","url":null,"abstract":"The long-tailed image classification task remains important in the development of deep neural networks as it explicitly deals with large imbalances in the class frequencies of the training data. While uncommon in engineered datasets, this imbalance is almost always present in real-world data. Previous approaches have shown that combining cross-entropy and contrastive learning can improve performance on the long-tailed task, but they do not explore the tradeoff between head and tail classes. We propose a novel class instance balanced loss (CIBL), which reweights the relative contributions of a cross-entropy and a contrastive loss as a function of the frequency of class instances in the training batch. This balancing favours the contrastive loss for more common classes, leading to a learned classifier with a more balanced performance across all class frequencies. Furthermore, increasing the relative weight on the contrastive head shifts performance from common (head) to rare (tail) classes, allowing the user to skew the performance towards these classes if desired. We also show that changing the linear classifier head with a cosine classifier yields a network that can be trained to similar performance in substantially fewer epochs. We obtain competitive results on both CIFAR-100-LT and ImageNet-LT.","PeriodicalId":325041,"journal":{"name":"2023 20th Conference on Robots and Vision (CRV)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131475465","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

Empirical Thresholding on Spatio-Temporal Autoencoders Trained on Surveillance Videos in a Dementia Care Unit 在痴呆护理单元监控视频上训练的时空自编码器的经验阈值

2023 20th Conference on Robots and Vision (CRV) Pub Date : 2023-06-01 DOI: 10.1109/CRV60082.2023.00041

Shehroz S. Khan, Pratik K. Mishra, B. Ye, Kristine Newman, A. Iaboni, Alex Mihailidis

{"title":"Empirical Thresholding on Spatio-Temporal Autoencoders Trained on Surveillance Videos in a Dementia Care Unit","authors":"Shehroz S. Khan, Pratik K. Mishra, B. Ye, Kristine Newman, A. Iaboni, Alex Mihailidis","doi":"10.1109/CRV60082.2023.00041","DOIUrl":"https://doi.org/10.1109/CRV60082.2023.00041","url":null,"abstract":"Agitation is a key behavioural and psychological symptom exhibited by people with dementia. These behaviours can put the patient with dementia and others' health and safety at risk. Surveillance cameras installed in long-term care facilities provide an opportunity to monitor patients continuously and flag behaviours of risks, including agitation. However, agitation behaviours occur rarely and diversely, leading to very small training data. Therefore, an anomaly detection approach is more suitable for this problem. In this paper, we train three baseline spatio-temporal convolutional autoencoders (on raw video, skeletons and segmentation mask) on 21 hours of normal activities and tested it on 9 hours of labelled normal and agitation data collected from a real patient in a dementia unit. The deployment of anomaly detection-based classifiers is challenging in real-world due to the absence of a validation set to obtain an operating threshold to regulate true positive and false positive rates. We present a new approach to create a proxy validation set for unseen agitation events utilizing the outliers within normal activities, and trained two separate autoencoders on normal and outliers activities. Then, we present 11 empirical thresholding approaches (existing, adapted and new) using either only normal training data or the proxy validation set. Our results showed consistently across raw video, skeletons and segmentation masks input that incorporating a proxy validation set improved performance both in terms of geometric means and Matthew's correlation coefficient. This paper highlights the real-world deployment challenges and assessment of the limit of true positives or false positives that can be acceptable in a clinical care environment.","PeriodicalId":325041,"journal":{"name":"2023 20th Conference on Robots and Vision (CRV)","volume":"477 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131983495","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

Naive Scene Graphs: How Visual is Modern Visual Relationship Detection? 朴素场景图:现代视觉关系检测有多视觉化?

2023 20th Conference on Robots and Vision (CRV) Pub Date : 2023-06-01 DOI: 10.1109/CRV60082.2023.00034

David Abou Chacra, J. Zelek

{"title":"Naive Scene Graphs: How Visual is Modern Visual Relationship Detection?","authors":"David Abou Chacra, J. Zelek","doi":"10.1109/CRV60082.2023.00034","DOIUrl":"https://doi.org/10.1109/CRV60082.2023.00034","url":null,"abstract":"Modern approaches to scene graph generation still struggle with their performance, with even state of the art approaches hovering under a 15% mean recall on certain evaluation modes. This poor performance is partially a result of networks heavily relying and fixating on non-visual data, such as class statistics, instead of the pixel-level signals present in the images. We demonstrate this by examining the 'visual-ness' of visual relationship detection approaches. We first describe and implement a new Naive Bayes-based statistical baseline for scene graph generation. Most notably, this basic classifier does not utilize the image pixels, but relies on the properties of the bounding boxes (class labels, topological configuration, … etc.) to predict the relationship labels. We demonstrate that our classical machine learning approach, one as simple as a categorical Naive Bayes classifier, can perform relationship detection in a manner that achieves relatively competitive performance to that of modern scene graph generators. This is an alarming finding regarding scene graph generation that implies that visual data in images may not be utilized in modern visual relationship detection past the point of object detection. We finally discuss how more visual modern approaches to scene graph generation appear to remedy some of these shortcomings.","PeriodicalId":325041,"journal":{"name":"2023 20th Conference on Robots and Vision (CRV)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128630610","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

What does the Occluding Contour Tell us about Quantitative Shape? 闭塞轮廓告诉我们关于定量形状的什么?

2023 20th Conference on Robots and Vision (CRV) Pub Date : 2023-06-01 DOI: 10.1109/CRV60082.2023.00015

Yiming Qian, J. Elder

引用次数: 0