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Correlation-Aware Deep Tracking

Correlation-Aware Deep Tracking

引用

ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xie, Fei Wang, Chunyu Wang, Guangting Cao, Yue Yang, Wankou Zeng, Wenjun Southeast Univ Nanjing Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Robustness and discrimination power are two fundamental requirements in visual object tracking. In most tracking paradigms, we find that the features extracted by the popular Siamese-like networks cannot fully discriminatively model the tracked targets and distractor objects, hindering them from simultaneously meeting these two requirements. While most methods focus on designing robust correlation operations, we propose a novel target-dependent feature network inspired by the self-/cross-attention scheme. In contrast to the Siamese-like feature extraction, our network deeply embeds cross-image feature correlation in multiple layers of the feature network. By extensively matching the features of the two images through multiple layers, it is able to suppress non-target features, resulting in instance-varying feature extraction. The output features of the search image can be directly used for predicting target locations without extra correlation step. Moreover, our model can be flexibly pre-trained on abundant unpaired images, leading to notably faster convergence than the existing methods. Extensive experiments show our method achieves the state-of-the-art results while running at real-time. Our feature networks also can be applied to existing tracking pipelines seamlessly to raise the tracking performance.

关键词： visualization Target tracking Correlation Systematics Pipelines Feature extraction Transformers

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Machine Learning Models for Life Expectancy

Machine Learning Models for Life Expectancy

引用

2023 International conference on Artificial Intelligence and Applications, ICAIA 2023 and Alliance Technology conference, ATCON-1 2023

作者： Jalan, Deepanshi Tuli, Anandita Chaudhary, Vanshika Sharma, Nonita Rakhra, Manik Indira Gandhi Dtu for Women Department of Electronics and Communication with Ai Delhi India Indira Gandhi Dtu for Women Department of Information Technology Delhi India Lovely Professional University Department of Computer Science and Engineering Phagwara India

ISBN: (纸本)9781665456272

Life expectancy (LE) models provide a lot of ways to improve healthcare and other social welfares related to society. Life expectancy models provide solutions to problems like how to decide on retirement age or manage financial issues related to public matters. These models are becoming prominent in many regions as they are being widely used by government bodies and private sector for their policy making and developing health integrated systems. Thus, this paper aims to analyze the Trends in Life Expectancy in about 72 countries of the world over a span of 16 years, i.e., from 2000-2015. The study gives plots of attributes such as life expectancy, GDP, infant deaths, adult mortality, etc. across year which would help the countries understand the life expectancy trends over the course of time and suggest areas which should be focused upon to efficiently increase the life expectancy of its population. The simulations are done in Google Collab by using various Python libraries like pandas, numpy, matplotlib (used for plotting graphs), seaborn (used for plotting 3-D graphs and advanced visualization features of python), sklearn (used for handling missing data), and plotly express (used for plotting choropleth). © 2023 ieee.

关键词： Python

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Synthetic Aperture Imaging with Events and Frames

Synthetic Aperture Imaging with Events and Frames

引用

ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Liao, Wei Zhang, Xiang Yu, Lei Lin, Shijie Yang, Wen Qiao, Ning Wuhan Univ Wuhan Peoples R China Univ Hong Kong Hong Kong Peoples R China Chengdu SynSense Tech Co Ltd Chengdu Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The Event-based Synthetic Aperture Imaging (E-SAI) has recently been proposed to see through extremely dense occlusions. However, the performance of E-SAI is not consistent under sparse occlusions due to the dramatic de-crease of signal events. This paper addresses this problem by leveraging the merits of both events and frames, leading to a fusion-based SAI (EF-SAI) that performs consistently under the different densities of occlusions. In particular, we first extract the feature from events and frames via multi-modal feature encoders and then apply a multi-stage fusion network for cross-modal enhancement and density-aware feature selection. Finally, a CNN decoder is employed to generate occlusion-free visual images from selected features. Extensive experiments show that our method effectively tackles varying densities of occlusions and achieves superior performance to the state-of-the-art SAI methods. Codes and datasets are available at https://***/smjsc/EF-SAI

关键词： Photography visualization Computer vision Codes Lighting Apertures Feature extraction

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Gravity-constrained point cloud registration

Gravity-constrained point cloud registration

引用

ieee/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Kubelka, Vladimir Vaidis, Maxime Pomerleau, Francois Univ Laval Northern Robot Lab Quebec City PQ Canada

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

Visual and lidar Simultaneous Localization and Mapping (SLAM) algorithms benefit from the Inertial Measurement Unit (IMU) modality. The high-rate inertial data complement the other lower-rate modalities. Moreover, in the absence of constant acceleration, the gravity vector makes two attitude angles out of three observable in the global coordinate frame. In visual odometry, this is already being used to reduce the 6-Degrees Of Freedom (DOF) pose estimation problem to 4-DOF. In lidar SLAM, the gravity measurements are often used as a penalty in the back-end global map optimization to prevent map deformations. In this work, we propose an Iterative Closest Point (ICP)-based front-end which exploits the observable DOF and provides pose estimates aligned with the gravity vector. We believe that this front-end has the potential to support the loop closure identification, thus speeding up convergences of global map optimizations. The presented approach has been extensively tested against accurate ground-truth localization in large-scale outdoor environments as well as in the Subterranean Challenge organized by Defense Advanced Research Projects Agency (DARPA). We show that it can reduce the localization drift by 30% when compared to the standard 6-DOF ICP. Moreover, the code is readily available to the community as a part of the libpointmatcher library.

关键词： Location awareness visualization Simultaneous localization and mapping Laser radar Codes Libraries Calibration

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A Novel Robot with Rolling and Climbing Modes for Power Transmission Line Inspection

A Novel Robot with Rolling and Climbing Modes for Power Tran...

引用

ieee/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Fu, Qiang Guan, Yisheng Zhu, Haifei Guangdong Univ Technol Sch Electromech Engn Biomimet & Intelligent Robot Lab BIRL Guangzhou Peoples R China

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

As a hard high-altitude work, power transmission line inspection increasingly demands robots to conduct in place of the human being. A variety of robots have been developed to this end, with basic locomotion and inspection implemented on the lines. However, most current line inspection robots (LIRs) are only mobile platforms with complex structures and large weights, lacking sufficiently dexterous locomotion on lines, especially for obstacle overcoming and line transition. Also with sensors fixed on the platform, the inspection range is largely limited. For higher mobility and a larger inspection range, a novel biped robot that can roll and climb on a power transmission line for inspection, called Climbot-L, is proposed in this paper. While the rolling mode has the advantage of high locomotion efficiency, the biped climbing mode makes it possible to easily overcome obstacles on the line, transition to adjacent cables, and have multi-view detection. In this paper, the design of this novel robot is first introduced, the working principle of the wheel-gripper modules is then analyzed, and obstacle overcoming gaits are stated. The effectiveness and high maneuverability of the presented robot are verified by a series of experiments.

关键词： Wireless communication Wireless sensor networks visualization Power cables Wheels Inspection Robot sensing systems

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Visual Pressure Estimation and Control for Soft Robotic Grippers

Visual Pressure Estimation and Control for Soft Robotic Grip...

引用

ieee/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Grady, Patrick Collins, Jeremy A. Brahmbhatt, Samarth Twigg, Christopher D. Tang, Chengcheng Hays, James Kemp, Charles C. Georgia Inst Technol Inst Robot & Intelligent Machines Atlanta GA 30332 USA Intel Labs Santa Clara CA 95054 USA Meta Real Labs Menlo Pk CA USA Hello Robot Inc Martinez CA USA GT Atlanta GA USA

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

Soft robotic grippers facilitate contact-rich manipulation, including robust grasping of varied objects. Yet the beneficial compliance of a soft gripper also results in significant deformation that can make precision manipulation challenging. We present visual pressure estimation & control (VPEC), a method that infers pressure applied by a soft gripper using an RGB image from an external camera. We provide results for visual pressure inference when a pneumatic gripper and a tendon-actuated gripper make contact with a flat surface. We also show that VPEC enables precision manipulation via closed-loop control of inferred pressure images. In our evaluation, a mobile manipulator (Stretch RE1 from Hello Robot) uses visual servoing to make contact at a desired pressure;follow a spatial pressure trajectory;and grasp small low-profile objects, including a microSD card, a penny, and a pill. Overall, our results show that visual estimates of applied pressure can enable a soft gripper to perform precision manipulation.

关键词： visualization Estimation Soft robotics Pneumatic systems Manipulators Visual servoing Trajectory

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Multi-Phase Multi-Modal Haptic Teleoperation

Multi-Phase Multi-Modal Haptic Teleoperation

引用

ieee/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Muehlbauer, Maximilian Steinmetz, Franz Stulp, Freek Hulin, Thomas Albu-Schaeffer, Alin Tech Univ Munich Dept Informat Sensor Based Robot Syst & Intellige Garching Germany German Aerosp Ctr DLR Robot & Mechatron Ctr RMC D-82234 Wessling Germany

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

Virtual Fixtures facilitate teleoperation, for instance by guiding the human operator. Developing these Virtual Fixtures in tasks with tight tolerances remains challenging. Fixtures with a high stiffness allow for more precise guidance, whereas a lower stiffness is required to allow for corrections. We observed that many assembly operations can be split into different phases - approaching, positioning, in-contact manipulation - each with different accuracy requirements. Therefore, we propose to use multi-modal fixtures, satisfying the different requirements of these phases: i.e. a position-based Trajectory Fixture for approaching and a more accurate Visual Servoing Fixture for the positioning phase. A state estimation and arbitration component ensures smooth transitions between the fixtures to provide optimal support for the operator and to achieve global availability paired with local precision at the same time. It also allows a high stiffness to be used throughout, thus achieving good guidance for all phases. The approach is validated in an application from a space scenario, consisting of the assembly of a CubeSat subsystem. The empirical results from a pilot study on this task show that our approach is faster and requires less interaction force from the operator than the baseline method.

关键词： visualization Uncertainty Machine vision Fixtures Visual servoing Trajectory Haptic interfaces

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Contrastive Learning for Cross-Domain Open World Recognition

Contrastive Learning for Cross-Domain Open World Recognition

引用

ieee/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Borlino, Francesco Cappio Bucci, Silvia Tommasi, Tatiana Politecn Torino DAUIN Dept Turin Italy Italian Inst Technol Turin Italy

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

The ability to evolve is fundamental for any valuable autonomous agent whose knowledge cannot remain limited to that injected by the manufacturer. Consider for example a home assistant robot: it should be able to incrementally learn new object categories when requested, but also to recognize the same objects in different environments (rooms) and poses (hand-held/on the floor/above furniture), while rejecting unknown ones. Despite its importance, this scenario has started to raise interest in the robotic community only recently and the related research is still in its infancy, with existing experimental testbeds but no tailored methods. With this work, we propose the first learning approach that deals with all the previously mentioned challenges at once by exploiting a single contrastive objective. We show how it learns a feature space perfectly suitable to incrementally include new classes and is able to capture knowledge which generalizes across a variety of visual domains. Our method is endowed with a tailored effective stopping criterion for each learning episode and exploits a selfpaced thresholding strategy that provides the classifier with a reliable rejection option. Both these novel contributions are based on the observation of the data statistics and do not need manual tuning. An extensive experimental analysis confirms the effectiveness of the proposed approach in establishing the new state-of-the-art. The code is available at https://github. com/FrancescoCappio/Contrastive_Open_World.

关键词： visualization Codes Manuals Autonomous agents Reliability Task analysis Tuning

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DetectorDetective: Investigating the Effects of Adversarial Examples on Object Detectors

DetectorDetective: Investigating the Effects of Adversarial ...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Vellaichamy, Sivapriya Hull, Matthew Wang, Zijie J. Das, Nilaksh Peng, Shengyun Park, Haekyu Chau, Duen Horng Polo Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

With deep learning based systems performing exceedingly well in many vision-related tasks, a major concern with their widespread deployment especially in safety-critical applications is their susceptibility to adversarial attacks. We propose DetectorDetective, an interactive visual tool that aims to help users better understand the behaviors of a model as adversarial images journey through an object detector. DetectorDetective enables users to easily learn about how the three key modules of the Faster R-CNN object detector - Feature Pyramidal Network, Region Proposal Network, and Region Of Interest Head - respond to a user-selected benign image and its adversarial version. visualizations about the progressive changes in the intermediate features among such modules help users gain insights into the impact of adversarial attacks, and perform side-by-side comparisons between the benign and adversarial responses. Furthermore, DetectorDetective displays saliency maps for the input images to comparatively highlight image regions that contribute to attack success. DetectorDetective complements adversarial machine learning research on object detection by providing a user-friendly interactive tool for inspecting and understanding model responses. DetectorDetective is available at the following public demo link: https://***/detector-detective. A video demo is available at https://***/5C3K1h87CZI.

关键词： visualization Head Detectors Object detection Feature extraction Magnetic heads Behavioral sciences

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Transferable 3D Adversarial Textures using End-to-end Optimization 22

Transferable 3D Adversarial Textures using End-to-end Optimi...

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22nd ieee/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Pestana, Camilo Akhtar, Naveed Rahnavard, Nazanin Shah, Mubarak Mian, Ajmal Univ Western Australia Nedlands WA Australia Univ Cent Florida Orlando FL 32816 USA

ISBN: (纸本)9781665409155

Deep visual models are known to be vulnerable to adversarial attacks. The last few years have seen numerous techniques to compute adversarial inputs for these models. However, there are still under-explored avenues in this critical research direction. Among those is the estimation of adversarial textures for 3D models in an end-to-end optimization scheme. In this paper, we propose such a scheme to generate adversarial textures for 3D models that are highly transferable and invariant to different camera views and lighting conditions. Our method makes use of neural rendering with explicit control over the model texture and background. We ensure transferability of the adversarial textures by employing an ensemble of robust and non-robust models. Our technique utilizes 3D models as a proxy to simulate closer to real-life conditions, in contrast to conventional use of 2D images for adversarial attacks. We show the efficacy of our method with extensive experiments.

关键词： Solid modeling visualization Computer vision Three-dimensional displays Computational modeling Pipelines Lighting

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