3D object tracking based on deep neural networks has a wide range of potential applications,such as autonomous driving and robotics.However,deep neural networks are vulnerable to adversarial examples.Traditionally,adv...3D object tracking based on deep neural networks has a wide range of potential applications,such as autonomous driving and robotics.However,deep neural networks are vulnerable to adversarial examples.Traditionally,adversarial examples are generated by applying perturbations to individual samples,which requires exhaustive calculations for each sample and thereby suffers from low efficiency during malicious attacks.Hence,the universal adversarial perturbation has been introduced,which is sample-agnostic.The universal perturbation is able to make classifiers misclassify most samples.In this paper,a topology-aware universal adversarial attack method against 3D object tracking is proposed,which can lead to predictions of a 3D tracker deviating from the ground truth in most scenarios.Specifically,a novel objective function consisting of a confidence loss,direction loss and distance loss generates an atomic perturbation from a tracking template,and aims to fail a tracking task.Subsequently,a series of atomic perturbations are iteratively aggregated to derive the universal adversarial perturbation.Furthermore,in order to address the characteristic of permutation invariance inherent in the point cloud data,the topology information of the tracking template is employed to guide the generation of the universal perturbation,which imposes correspondences between consecutively generated perturbations.The generated universal perturbation is designed to be aware of the topology of the targeted tracking template during its construction and application,thus leading to superior attack performance.Experiments on the KITTI dataset demonstrate that the performance of 3D object tracking can be significantly degraded by the proposed method.展开更多
Augmented Reality(AR)applications can be used to improve tasks and mitigate errors during facilities operation and maintenance.This article presents an AR system for facility management using a three-dimensional(3D)ob...Augmented Reality(AR)applications can be used to improve tasks and mitigate errors during facilities operation and maintenance.This article presents an AR system for facility management using a three-dimensional(3D)object tracking method.Through spatial mapping,the object of interest,a pipe trap underneath a sink,is tracked and mixed onto the AR visualization.From that,the maintenance steps are transformed into visible and animated instructions.Although some tracking issues related to the component parts were observed,the designed AR application results demonstrated the potential to improve facility management tasks.展开更多
Center point localization is a major factor affecting the performance of 3D single object tracking.Point clouds themselves are a set of discrete points on the local surface of an object,and there is also a lot of nois...Center point localization is a major factor affecting the performance of 3D single object tracking.Point clouds themselves are a set of discrete points on the local surface of an object,and there is also a lot of noise in the labeling.Therefore,directly regressing the center coordinates is not very reasonable.Existing methods usually use volumetric-based,point-based,and view-based methods,with a relatively single modality.In addition,the sampling strategies commonly used usually result in the loss of object information,and holistic and detailed information is beneficial for object localization.To address these challenges,we propose a novel Multi-view unsupervised center Uncertainty 3D single object Tracker(MUT).MUT models the potential uncertainty of center coordinates localization using an unsupervised manner,allowing the model to learn the true distribution.By projecting point clouds,MUT can obtain multi-view depth map features,realize efficient knowledge transfer from 2D to 3D,and provide another modality information for the tracker.We also propose a former attraction probability sampling strategy that preserves object information.By using both holistic and detailed descriptors of point clouds,the tracker can have a more comprehensive understanding of the tracking environment.Experimental results show that the proposed MUT network outperforms the baseline models on the KITTI dataset by 0.8%and 0.6%in precision and success rate,respectively,and on the NuScenes dataset by 1.4%,and 6.1%in precision and success rate,respectively.The code is made available at https://github.com/abchears/MUT.git.展开更多
基金supported by the National Natural Science Foundation of China(No.62072076)the Sichuan Provincial Research Plan Project(No.2022ZDZX0005).
文摘3D object tracking based on deep neural networks has a wide range of potential applications,such as autonomous driving and robotics.However,deep neural networks are vulnerable to adversarial examples.Traditionally,adversarial examples are generated by applying perturbations to individual samples,which requires exhaustive calculations for each sample and thereby suffers from low efficiency during malicious attacks.Hence,the universal adversarial perturbation has been introduced,which is sample-agnostic.The universal perturbation is able to make classifiers misclassify most samples.In this paper,a topology-aware universal adversarial attack method against 3D object tracking is proposed,which can lead to predictions of a 3D tracker deviating from the ground truth in most scenarios.Specifically,a novel objective function consisting of a confidence loss,direction loss and distance loss generates an atomic perturbation from a tracking template,and aims to fail a tracking task.Subsequently,a series of atomic perturbations are iteratively aggregated to derive the universal adversarial perturbation.Furthermore,in order to address the characteristic of permutation invariance inherent in the point cloud data,the topology information of the tracking template is employed to guide the generation of the universal perturbation,which imposes correspondences between consecutively generated perturbations.The generated universal perturbation is designed to be aware of the topology of the targeted tracking template during its construction and application,thus leading to superior attack performance.Experiments on the KITTI dataset demonstrate that the performance of 3D object tracking can be significantly degraded by the proposed method.
文摘Augmented Reality(AR)applications can be used to improve tasks and mitigate errors during facilities operation and maintenance.This article presents an AR system for facility management using a three-dimensional(3D)object tracking method.Through spatial mapping,the object of interest,a pipe trap underneath a sink,is tracked and mixed onto the AR visualization.From that,the maintenance steps are transformed into visible and animated instructions.Although some tracking issues related to the component parts were observed,the designed AR application results demonstrated the potential to improve facility management tasks.
文摘Center point localization is a major factor affecting the performance of 3D single object tracking.Point clouds themselves are a set of discrete points on the local surface of an object,and there is also a lot of noise in the labeling.Therefore,directly regressing the center coordinates is not very reasonable.Existing methods usually use volumetric-based,point-based,and view-based methods,with a relatively single modality.In addition,the sampling strategies commonly used usually result in the loss of object information,and holistic and detailed information is beneficial for object localization.To address these challenges,we propose a novel Multi-view unsupervised center Uncertainty 3D single object Tracker(MUT).MUT models the potential uncertainty of center coordinates localization using an unsupervised manner,allowing the model to learn the true distribution.By projecting point clouds,MUT can obtain multi-view depth map features,realize efficient knowledge transfer from 2D to 3D,and provide another modality information for the tracker.We also propose a former attraction probability sampling strategy that preserves object information.By using both holistic and detailed descriptors of point clouds,the tracker can have a more comprehensive understanding of the tracking environment.Experimental results show that the proposed MUT network outperforms the baseline models on the KITTI dataset by 0.8%and 0.6%in precision and success rate,respectively,and on the NuScenes dataset by 1.4%,and 6.1%in precision and success rate,respectively.The code is made available at https://github.com/abchears/MUT.git.
