In visible light positioning systems,some scholars have proposed target tracking algorithms to balance the relationship among positioning accuracy,real-time performance,and robustness.However,there are still two probl...In visible light positioning systems,some scholars have proposed target tracking algorithms to balance the relationship among positioning accuracy,real-time performance,and robustness.However,there are still two problems:(1)When the captured LED disappears and the uncertain LED reappears,existing tracking algorithms may recognize the landmark in error;(2)The receiver is not always able to achieve positioning under various moving statuses.In this paper,we propose an enhanced visual target tracking algorithm to solve the above problems.First,we design the lightweight recognition/demodulation mechanism,which combines Kalman filtering with simple image preprocessing to quickly track and accurately demodulate the landmark.Then,we use the Gaussian mixture model and the LED color feature to enable the system to achieve positioning,when the receiver is under various moving statuses.Experimental results show that our system can achieve high-precision dynamic positioning and improve the system’s comprehensive performance.展开更多
It is of great significance to rapidly detect targets in large-field remote sensing images,with limited computation resources.Employing relative achievements of visual attention in perception psychology,this paper pro...It is of great significance to rapidly detect targets in large-field remote sensing images,with limited computation resources.Employing relative achievements of visual attention in perception psychology,this paper proposes a hierarchical attention based model for target detection.Specifically,at the preattention stage,before getting salient regions,a fast computational approach is applied to build a saliency map.After that,the focus of attention（FOA） can be quickly obtained to indicate the salient objects.Then,at the attention stage,under the FOA guidance,the high-level visual features of the region of interest are extracted in parallel.Finally,at the post-attention stage,by integrating these parallel and independent visual attributes,a decision-template based classifier fusion strategy is proposed to discriminate the task-related targets from the other extracted salient objects.For comparison,experiments on ship detection are done for validating the effectiveness and feasibility of the proposed model.展开更多
This paper introduces an approach for visual tracking of multi-target with occlusion occurrence. Based on the author's previous work in which the Overlap Coefficient (OC) is used to detect the occlusion, in this p...This paper introduces an approach for visual tracking of multi-target with occlusion occurrence. Based on the author's previous work in which the Overlap Coefficient (OC) is used to detect the occlusion, in this paper a method of combining Bhattacharyya Coefficient (BC) and Kalman filter innovation term is proposed as the criteria for jointly detecting the occlusion occurrence. Fragmentation of target is introduced in order to closely monitor the occlusion development. In the course of occlusion, the Kalman predictor is applied to determine the location of the occluded target, and the criterion for checking the re-appearance of the occluded target is also presented. The proposed approach is put to test on a standard video sequence, suggesting the satisfactory performance in multi-target tracking.展开更多
Synthetic Aperture Radar(SAR) imaging systems have been widely used in civil and military fields due to their all-weather and all-day abilities and various other advantages. However, due to image data exponentially in...Synthetic Aperture Radar(SAR) imaging systems have been widely used in civil and military fields due to their all-weather and all-day abilities and various other advantages. However, due to image data exponentially increasing, there is a need for novel automatic target detection and recognition technologies. In recent years, the visual attention mechanism in the visual system has helped humans effectively deal with complex visual signals. In particular, biologically inspired top-down attention models have garnered much attention recently. This paper presents a visual attention model for SAR target detection, comprising a bottom-up stage and top-down process.In the bottom-up step, the Itti model is improved based on the difference between SAR and optical images. The top-down step fully utilizes prior information to further detect targets. Extensive detection experiments carried out on the benchmark Moving and Stationary Target Acquisition and Recognition(MSTAR) dataset show that, compared with typical visual models and other popular detection methods, our model has increased ability and robustness for SAR target detection, under a range of Signal to Clutter Ratio(SCR) conditions and scenes. In addition, results obtained using only the bottom-up stage are inferior to those of the proposed method, further demonstrating the effectiveness and rationality of a top-down strategy. In summary, our proposed visual attention method can be considered a potential benchmark resource for the SAR research community.展开更多
【目的】针对机器人自主装配研究多聚焦静态目标,移动目标装配相关研究不足的问题,提出一种视觉阻抗控制器的变参数控制方法,以提升机器人在动态环境中目标跟踪与移动装配的适应性和柔顺性。【方法】首先,结合力反馈与视觉反馈信息,在...【目的】针对机器人自主装配研究多聚焦静态目标,移动目标装配相关研究不足的问题,提出一种视觉阻抗控制器的变参数控制方法,以提升机器人在动态环境中目标跟踪与移动装配的适应性和柔顺性。【方法】首先,结合力反馈与视觉反馈信息,在特征空间构建2阶阻抗控制器,为动态装配提供控制基础;其次,将蒙特卡洛随机失活(Monte Carlo Dropout,MCD)作为概率动力学模型,改进概率推理学习控制优化(Probabilistic Inference for Learning Control Optimization,PILCO)算法,平衡状态不确定性推理能力与计算效率;最后,以特征误差和机器人关节位置信息为观测空间,自适应调整控制器阻抗参数,优化动态跟踪与装配性能。【结果】仿真及试验台验证结果表明,相比原高斯过程模型,MCD模型在保留状态不确定性推理能力的同时,显著缩短训练时间(50轮训练时间从43.50 h降至12.82 h);装配成功率从94.5%提升至98.0%,平均装配用时从5.820 s缩短至3.253 s,超调量大幅减少,跟踪响应更及时。研究可为移动目标柔顺装配提供参考。展开更多
针对沙滩水域环境复杂且难以有效清洁的问题,综合应用人工智能物联网(Artificial Internet of Things, AIoT)和视觉识别等先进技术,开发了一款具有远程控制、视觉识别、智能抓取、重量检测及智能显示等功能的多功能清洁机器人。该机器...针对沙滩水域环境复杂且难以有效清洁的问题,综合应用人工智能物联网(Artificial Internet of Things, AIoT)和视觉识别等先进技术,开发了一款具有远程控制、视觉识别、智能抓取、重量检测及智能显示等功能的多功能清洁机器人。该机器人专为提升清洁效率和自动化水平设计,配备了远程控制、视觉识别、智能抓取、重量检测及状态显示等功能。采用英伟达Jetson Nano作为核心处理器,结合Intel D415深度相机和基于FloW数据集训练的YOLOv8算法,实现水面漂浮垃圾的实时检测与精确定位。系统通过STM32微控制器解析视觉数据并控制机械臂完成精准抓取。为提高移动性能,机器人采用麦克纳姆轮实现全向运动,当内置称重传感器检测到收集装置满载时,系统可自主返回基地卸载垃圾。此外,系统集成HC-05蓝牙模块实现远程无线控制,并通过OLED显示屏实时显示工作状态。综合应用了AIoT、自动化控制及视觉识别技术,突破了传统清洁方式的局限,显著提升了沙滩水域清洁工作的效率和便捷性,为环保行动提供了强有力的工具。展开更多
深度神经网络在很多应用领域取得了显著成功。然而,近年来的研究表明,它们容易受到对抗攻击的威胁。尤其是有目标的对抗攻击,能够精确控制未知模型的输出,对数据隐私和系统安全构成严重挑战。生成式攻击方法因其高效生成对抗样本的能力...深度神经网络在很多应用领域取得了显著成功。然而,近年来的研究表明,它们容易受到对抗攻击的威胁。尤其是有目标的对抗攻击,能够精确控制未知模型的输出,对数据隐私和系统安全构成严重挑战。生成式攻击方法因其高效生成对抗样本的能力,近年来逐渐应用于有目标攻击的研究中。然而,现有的生成式攻击方法通常针对单一目标类别生成对抗样本,在多目标任务中表现出计算效率低下、灵活性不足和扩展性有限等问题。针对这些不足,提出了一种基于双重信息的多目标生成式攻击(multitarget generative attack based on dual-information,MTGA-DI)方法。该方法通过设计一个条件生成模型,充分融合目标类别的语义和视觉信息,不仅具备多目标攻击能力,还显著提升了对抗样本的迁移性和鲁棒性。实验结果表明,与现有多目标攻击方法相比,MTGA-DI在标准训练模型和鲁棒模型上的性能更优,在应对基于输入预处理的防御模型时也展现出更强的适应能力。展开更多
Radar Maneuvering Targets Tracking(RMTT) in clutter is a quite challenging issue due to the errors in the models and the varying dynamics of the processes. Modern radar tracking system calls for the adaptive signal an...Radar Maneuvering Targets Tracking(RMTT) in clutter is a quite challenging issue due to the errors in the models and the varying dynamics of the processes. Modern radar tracking system calls for the adaptive signal and data processing algorithm urgently to adapt the uncertainty of the environment. The mechanism of human cognition can help persons cope with the similar diffi-culties in visual tracking. Inspired by human cognition mechanism, a comprehensive method for RMTT is proposed. In the method, the model transition probability in Interacting Multiple Model(IMM) and the validation gate can be adjusted dynamically with target maneuver;the waveform in radar transmitter can vary with the perception of the environment. Experimental results in cluttered scenes show that the proposed algorithm is more accurate for perceiving the environment and targets, and the waveform selection algorithm is better than that with fixed waveform.展开更多
基金supported by the Guangdong Basic and Applied Basic Research Foundation No.2021A1515110958National Natural Science Foundation of China No.62202215+1 种基金SYLU introduced high-level talents scientific research support plan,Chongqing University Innovation Research Group(CXQT21019)Chongqing Talents Project(CQYC201903048)。
文摘In visible light positioning systems,some scholars have proposed target tracking algorithms to balance the relationship among positioning accuracy,real-time performance,and robustness.However,there are still two problems:(1)When the captured LED disappears and the uncertain LED reappears,existing tracking algorithms may recognize the landmark in error;(2)The receiver is not always able to achieve positioning under various moving statuses.In this paper,we propose an enhanced visual target tracking algorithm to solve the above problems.First,we design the lightweight recognition/demodulation mechanism,which combines Kalman filtering with simple image preprocessing to quickly track and accurately demodulate the landmark.Then,we use the Gaussian mixture model and the LED color feature to enable the system to achieve positioning,when the receiver is under various moving statuses.Experimental results show that our system can achieve high-precision dynamic positioning and improve the system’s comprehensive performance.
基金supported by the National Natural Science Foundation of China (40871157)
文摘It is of great significance to rapidly detect targets in large-field remote sensing images,with limited computation resources.Employing relative achievements of visual attention in perception psychology,this paper proposes a hierarchical attention based model for target detection.Specifically,at the preattention stage,before getting salient regions,a fast computational approach is applied to build a saliency map.After that,the focus of attention（FOA） can be quickly obtained to indicate the salient objects.Then,at the attention stage,under the FOA guidance,the high-level visual features of the region of interest are extracted in parallel.Finally,at the post-attention stage,by integrating these parallel and independent visual attributes,a decision-template based classifier fusion strategy is proposed to discriminate the task-related targets from the other extracted salient objects.For comparison,experiments on ship detection are done for validating the effectiveness and feasibility of the proposed model.
基金Supported by the Program for Technology Innovation Team of Ningbo Government (No. 2011B81002)the Ningbo University Science Research Foundation (No.xkl11075)
文摘This paper introduces an approach for visual tracking of multi-target with occlusion occurrence. Based on the author's previous work in which the Overlap Coefficient (OC) is used to detect the occlusion, in this paper a method of combining Bhattacharyya Coefficient (BC) and Kalman filter innovation term is proposed as the criteria for jointly detecting the occlusion occurrence. Fragmentation of target is introduced in order to closely monitor the occlusion development. In the course of occlusion, the Kalman predictor is applied to determine the location of the occluded target, and the criterion for checking the re-appearance of the occluded target is also presented. The proposed approach is put to test on a standard video sequence, suggesting the satisfactory performance in multi-target tracking.
基金supported by the National Natural Science Foundation of China(Nos.61771027,61071139,61471019,61671035)supported in part under the Royal Society of Edinburgh-National Natural Science Foundation of China(RSE-NNSFC)Joint Project(2017–2019)(No.6161101383)with China University of Petroleum(Huadong)partially supported by the UK Engineering and Physical Sciences Research Council(EPSRC)(Nos.EP/I009310/1,EP/M026981/1)
文摘Synthetic Aperture Radar(SAR) imaging systems have been widely used in civil and military fields due to their all-weather and all-day abilities and various other advantages. However, due to image data exponentially increasing, there is a need for novel automatic target detection and recognition technologies. In recent years, the visual attention mechanism in the visual system has helped humans effectively deal with complex visual signals. In particular, biologically inspired top-down attention models have garnered much attention recently. This paper presents a visual attention model for SAR target detection, comprising a bottom-up stage and top-down process.In the bottom-up step, the Itti model is improved based on the difference between SAR and optical images. The top-down step fully utilizes prior information to further detect targets. Extensive detection experiments carried out on the benchmark Moving and Stationary Target Acquisition and Recognition(MSTAR) dataset show that, compared with typical visual models and other popular detection methods, our model has increased ability and robustness for SAR target detection, under a range of Signal to Clutter Ratio(SCR) conditions and scenes. In addition, results obtained using only the bottom-up stage are inferior to those of the proposed method, further demonstrating the effectiveness and rationality of a top-down strategy. In summary, our proposed visual attention method can be considered a potential benchmark resource for the SAR research community.
文摘【目的】针对机器人自主装配研究多聚焦静态目标,移动目标装配相关研究不足的问题,提出一种视觉阻抗控制器的变参数控制方法,以提升机器人在动态环境中目标跟踪与移动装配的适应性和柔顺性。【方法】首先,结合力反馈与视觉反馈信息,在特征空间构建2阶阻抗控制器,为动态装配提供控制基础;其次,将蒙特卡洛随机失活(Monte Carlo Dropout,MCD)作为概率动力学模型,改进概率推理学习控制优化(Probabilistic Inference for Learning Control Optimization,PILCO)算法,平衡状态不确定性推理能力与计算效率;最后,以特征误差和机器人关节位置信息为观测空间,自适应调整控制器阻抗参数,优化动态跟踪与装配性能。【结果】仿真及试验台验证结果表明,相比原高斯过程模型,MCD模型在保留状态不确定性推理能力的同时,显著缩短训练时间(50轮训练时间从43.50 h降至12.82 h);装配成功率从94.5%提升至98.0%,平均装配用时从5.820 s缩短至3.253 s,超调量大幅减少,跟踪响应更及时。研究可为移动目标柔顺装配提供参考。
文摘针对沙滩水域环境复杂且难以有效清洁的问题,综合应用人工智能物联网(Artificial Internet of Things, AIoT)和视觉识别等先进技术,开发了一款具有远程控制、视觉识别、智能抓取、重量检测及智能显示等功能的多功能清洁机器人。该机器人专为提升清洁效率和自动化水平设计,配备了远程控制、视觉识别、智能抓取、重量检测及状态显示等功能。采用英伟达Jetson Nano作为核心处理器,结合Intel D415深度相机和基于FloW数据集训练的YOLOv8算法,实现水面漂浮垃圾的实时检测与精确定位。系统通过STM32微控制器解析视觉数据并控制机械臂完成精准抓取。为提高移动性能,机器人采用麦克纳姆轮实现全向运动,当内置称重传感器检测到收集装置满载时,系统可自主返回基地卸载垃圾。此外,系统集成HC-05蓝牙模块实现远程无线控制,并通过OLED显示屏实时显示工作状态。综合应用了AIoT、自动化控制及视觉识别技术,突破了传统清洁方式的局限,显著提升了沙滩水域清洁工作的效率和便捷性,为环保行动提供了强有力的工具。
文摘深度神经网络在很多应用领域取得了显著成功。然而,近年来的研究表明,它们容易受到对抗攻击的威胁。尤其是有目标的对抗攻击,能够精确控制未知模型的输出,对数据隐私和系统安全构成严重挑战。生成式攻击方法因其高效生成对抗样本的能力,近年来逐渐应用于有目标攻击的研究中。然而,现有的生成式攻击方法通常针对单一目标类别生成对抗样本,在多目标任务中表现出计算效率低下、灵活性不足和扩展性有限等问题。针对这些不足,提出了一种基于双重信息的多目标生成式攻击(multitarget generative attack based on dual-information,MTGA-DI)方法。该方法通过设计一个条件生成模型,充分融合目标类别的语义和视觉信息,不仅具备多目标攻击能力,还显著提升了对抗样本的迁移性和鲁棒性。实验结果表明,与现有多目标攻击方法相比,MTGA-DI在标准训练模型和鲁棒模型上的性能更优,在应对基于输入预处理的防御模型时也展现出更强的适应能力。
基金co-supported by the National Natural Science Foundation of China(No.61671453)the Anhui Province Natural Science Fund Project,China(No.1608085MF123)
文摘Radar Maneuvering Targets Tracking(RMTT) in clutter is a quite challenging issue due to the errors in the models and the varying dynamics of the processes. Modern radar tracking system calls for the adaptive signal and data processing algorithm urgently to adapt the uncertainty of the environment. The mechanism of human cognition can help persons cope with the similar diffi-culties in visual tracking. Inspired by human cognition mechanism, a comprehensive method for RMTT is proposed. In the method, the model transition probability in Interacting Multiple Model(IMM) and the validation gate can be adjusted dynamically with target maneuver;the waveform in radar transmitter can vary with the perception of the environment. Experimental results in cluttered scenes show that the proposed algorithm is more accurate for perceiving the environment and targets, and the waveform selection algorithm is better than that with fixed waveform.
