Pedestrian detection is one of the most important problems in the visual sensor network. Considering that the visual sensors have limited cap ability, we propose a pedestrian detection method with low energy consumpti...Pedestrian detection is one of the most important problems in the visual sensor network. Considering that the visual sensors have limited cap ability, we propose a pedestrian detection method with low energy consumption. Our method contains two parts: one is an Enhanced Self-Organizing Background Subtraction (ESOBS) based foreground segmentation module to obtain active areas in the observed region from the visual sensors; the other is an appearance model based detection module to detect the pedestrians from the foreground areas. Moreover, we create our own large pedestrian dataset according to the specific scene in the visual sensor network. Numerous experiments are conducted in both indoor and outdoor specific scenes. The experimental results show that our method is effective.展开更多
As a component of Wireless Sensor Network(WSN),Visual-WSN(VWSN)utilizes cameras to obtain relevant data including visual recordings and static images.Data from the camera is sent to energy efficient sink to extract ke...As a component of Wireless Sensor Network(WSN),Visual-WSN(VWSN)utilizes cameras to obtain relevant data including visual recordings and static images.Data from the camera is sent to energy efficient sink to extract key-information out of it.VWSN applications range from health care monitoring to military surveillance.In a network with VWSN,there are multiple challenges to move high volume data from a source location to a target and the key challenges include energy,memory and I/O resources.In this case,Mobile Sinks(MS)can be employed for data collection which not only collects information from particular chosen nodes called Cluster Head(CH),it also collects data from nearby nodes as well.The innovation of our work is to intelligently decide on a particular node as CH whose selection criteria would directly have an impact on QoS parameters of the system.However,making an appropriate choice during CH selection is a daunting task as the dynamic and mobile nature of MSs has to be taken into account.We propose Genetic Machine Learning based Fuzzy system for clustering which has the potential to simulate human cognitive behavior to observe,learn and understand things from manual perspective.Proposed architecture is designed based on Mamdani’s fuzzy model.Following parameters are derived based on the model residual energy,node centrality,distance between the sink and current position,node centrality,node density,node history,and mobility of sink as input variables for decision making in CH selection.The inputs received have a direct impact on the Fuzzy logic rules mechanism which in turn affects the accuracy of VWSN.The proposed work creates a mechanism to learn the fuzzy rules using Genetic Algorithm(GA)and to optimize the fuzzy rules base in order to eliminate irrelevant and repetitive rules.Genetic algorithmbased machine learning optimizes the interpretability aspect of fuzzy system.Simulation results are obtained using MATLAB.The result shows that the classification accuracy increase along with minimizing fuzzy rules count and thus it can be inferred that the suggested methodology has a better protracted lifetime in contrast with Low Energy Adaptive Clustering Hierarchy(LEACH)and LEACHExpected Residual Energy(LEACH-ERE).展开更多
First,the constitution of traditional visual sensor is presented.The linear camera model is introduced and the transform matrix between the image coordinate system and the world coordinate system is established.The ba...First,the constitution of traditional visual sensor is presented.The linear camera model is introduced and the transform matrix between the image coordinate system and the world coordinate system is established.The basic principle of camera calibration is expatiated based on the linear camera model. On the basis of a detailed analysis of camera model,a new-style visual sensor for measurement is advanced.It can realize the real time control of the zoom of camera lens by step motor according to the size of objects.Moreover,re-calibration could be avoided and the transform matrix can be acquired by calculating,which can greatly simplify camera calibration process and save the time. Clearer images are gained,so the measurement system precision could be greatly improved.The basic structure of the visual sensor zoom is introduced,including the constitute mode and the movement rule of the fixed former part,zoom part,compensatory part and the fixed latter port.The realization method of zoom controlled by step motor is introduced. Finally,the constitution of the new-style visual sensor is introduced,including hardware and software.The hardware system is composed by manual zoom,CCD camera,image card,gearing,step motor,step motor driver and computer.The realization of software is introduced,including the composed module of software and the workflow of measurement system in the form of structured block diagram.展开更多
Wireless visual sensor network (VSN) can be said to be a special class of wireless sensor network (WSN) with smart-cameras. Due to its visual sensing capability, it has become an effective tool for applications such a...Wireless visual sensor network (VSN) can be said to be a special class of wireless sensor network (WSN) with smart-cameras. Due to its visual sensing capability, it has become an effective tool for applications such as large area surveillance, environmental monitoring and objects tracking. Different from a conventional WSN, VSN typically includes relatively expensive camera sensors, enhanced flash memory and a powerful CPU. While energy consumption is dominated primarily by data transmission and reception, VSN consumes extra power onimage sensing, processing and storing operations. The well-known energy-hole problem of WSNs has a drastic impact on the lifetime of VSN, because of the additional energy consumption of a VSN. Most prior research on VSN energy issues are primarily focusedon a single device or a given specific scenario. In this paper, we propose a novel optimal two-tier deployment strategy for a large scale VSN. Our two-tier VSN architecture includes tier-1 sensing network with visual sensor nodes (VNs) and tier-2 network having only relay nodes (RNs). While sensing network mainly performs image data collection, relay network only for wards image data packets to the central sink node. We use uniform random distribution of VNs to minimize the cost of VSN and RNs are deployed following two dimensional Gaussian distribution so as to avoid energy-hole problem. Algorithms are also introduced that optimizes deployment parameters and are shown to enhance the lifetime of the VSN in a cost effective manner.展开更多
Most sensors or cameras discussed in the sensor network community are usually 3D homogeneous, even though their2 D coverage areas in the ground plane are heterogeneous. Meanwhile, observed objects of camera networks a...Most sensors or cameras discussed in the sensor network community are usually 3D homogeneous, even though their2 D coverage areas in the ground plane are heterogeneous. Meanwhile, observed objects of camera networks are usually simplified as 2D points in previous literature. However in actual application scenes, not only cameras are always heterogeneous with different height and action radiuses, but also the observed objects are with 3D features（i.e., height）. This paper presents a sensor planning formulation addressing the efficiency enhancement of visual tracking in 3D heterogeneous camera networks that track and detect people traversing a region. The problem of sensor planning consists of three issues：（i） how to model the 3D heterogeneous cameras;（ii） how to rank the visibility, which ensures that the object of interest is visible in a camera＇s field of view;（iii） how to reconfigure the 3D viewing orientations of the cameras. This paper studies the geometric properties of 3D heterogeneous camera networks and addresses an evaluation formulation to rank the visibility of observed objects. Then a sensor planning method is proposed to improve the efficiency of visual tracking. Finally, the numerical results show that the proposed method can improve the tracking performance of the system compared to the conventional strategies.展开更多
智能化焊接在推进“工业强基”工程、支撑国家建设及国防安全中起到重要作用,从重大装备到精细结构,焊接都是不可或缺的关键技术,而机器人作为智能化焊接的重要载体,推动“以机器代替人,以机器解放人”的过程中将发挥重要作用。文中从...智能化焊接在推进“工业强基”工程、支撑国家建设及国防安全中起到重要作用,从重大装备到精细结构,焊接都是不可或缺的关键技术,而机器人作为智能化焊接的重要载体,推动“以机器代替人,以机器解放人”的过程中将发挥重要作用。文中从焊接制造全流程的场景建模、焊接过程形性原位感知、自适应调控、工艺知识构建等关键技术出发,重点阐述了焊接机器人的“免示教”编程环境感知、点云配准、焊缝轨迹规划和焊道自适应编排等共性技术的研究现状,以智能化焊接制造过程多源信息监测及控制系统为例,提出了基于IIOT-MAS(industrial internet of things-multi-agent system)焊接制造系统分层结构模型,介绍了焊接多模态信息感知、融合及工艺知识建模等共性科学问题,并介绍了工程机械部件焊接现场感知数据在线学习和模型-数据双驱动的焊接质量评价模型典型案例,探讨了机器人焊接智能化的发展趋势和所面临的挑战。展开更多
水下同步定位与建图(Simultaneous Localization and Mapping,SLAM)技术使水下机器人能在未知水下环境中同时进行自我定位和环境地图构建,对海洋学研究、海底资源勘探等领域具有重要意义。本文综述了水下视觉SLAM技术最新研究进展、挑...水下同步定位与建图(Simultaneous Localization and Mapping,SLAM)技术使水下机器人能在未知水下环境中同时进行自我定位和环境地图构建,对海洋学研究、海底资源勘探等领域具有重要意义。本文综述了水下视觉SLAM技术最新研究进展、挑战与解决方案及未来研究方向,梳理了水下视觉SLAM的关键理论。水下环境的复杂性,如光线衰减、散射和水流影响,为水下SLAM的研究带来挑战。本文分析了水下视觉SLAM的最新研究进展,包括多传感器融合、深度学习技术及优化算法的应用,这些技术提高了水下SLAM系统的鲁棒性和精度。同时,本文还探讨了水下SLAM技术面临的主要挑战,并提出了可能的解决方案,如提高传感器数据的准确性、增强数据融合算法的实时性和鲁棒性、改进特征提取与匹配方法,以及提升定位与建图算法的精度和稳定性。最后,本文对水下SLAM的未来研究方向进行了展望,包括新型传感器技术、人工智能技术的应用和水下多机器人协同SLAM的发展,旨在提供该领域科研与技术发展的整体视角。展开更多
目前水果采摘存在劳动力短缺、采摘效率低和作业环境复杂等问题,亟须发展具备高精度感知与自主作业能力的智能化采摘装备,以全面提升果实采摘的效率和质量。传感器技术在水果采摘机器人中的应用包括路径规划、果实识别、定位及抓取控制...目前水果采摘存在劳动力短缺、采摘效率低和作业环境复杂等问题,亟须发展具备高精度感知与自主作业能力的智能化采摘装备,以全面提升果实采摘的效率和质量。传感器技术在水果采摘机器人中的应用包括路径规划、果实识别、定位及抓取控制等关键任务环节。针对非结构化果园环境,视觉、触觉与激光传感器的协同应用可实现目标识别、位置感知与避障控制,显著提升了采摘机器人对复杂环境的适应能力与作业精度,但是现有传感器仍然存在一些技术短板,如视觉传感器易受阳光干扰、枝叶遮挡和果实密集分布等因素影响,导致目标检测困难;触觉传感器易受温湿度影响,难以量化复杂的力学反馈,因而细微抓取力控制困难;由于非结构化环境下的路径优化困难,且激光传感器成本高昂,限制了其大规模应用。同时,单一传感器存在感知维度单一、环境适应性不足和果实特征感知不足等局限,难以适应非结构化果园环境。为此,针对多传感器融合技术面临的数据异构性、时序同步性和计算复杂性等挑战,对传感器技术在水果采摘机器人的未来应用进行了展望,指出融合红外、紫外等多波段成像技术和高动态范围(high dynamic range imaging,HDR)成像技术,柔性电子皮肤结合仿生结构设计的多传感器融合技术有望得到广泛应用。展开更多
The relative pose between inertial and visual sensors equipped in autonomous robots is calibrated in two steps. In the first step, the sensing system is moved along a line, the orientations in the relative pose are co...The relative pose between inertial and visual sensors equipped in autonomous robots is calibrated in two steps. In the first step, the sensing system is moved along a line, the orientations in the relative pose are computed from at least five corresponding points in the two images captured before and after the movement. In the second step, the translation parameters in the relative pose are obtained with at least two corresponding points in the two images captured before and after one step motion. Experiments are conducted to verify the effectiveness of the proposed method.展开更多
基金This paper was supported partially by the Natural Science Foundation of China under Grants No. 60833009, No. 61003280 the National Science Fund for Distinguished Young Scholars under Grant No. 60925010+1 种基金 the Funds for Creative Research Groups of China under Grant No.61121001 the Pro- gram for Changjiang Scholars and Innovative Research Team in University under Grant No. IRT1049.
文摘Pedestrian detection is one of the most important problems in the visual sensor network. Considering that the visual sensors have limited cap ability, we propose a pedestrian detection method with low energy consumption. Our method contains two parts: one is an Enhanced Self-Organizing Background Subtraction (ESOBS) based foreground segmentation module to obtain active areas in the observed region from the visual sensors; the other is an appearance model based detection module to detect the pedestrians from the foreground areas. Moreover, we create our own large pedestrian dataset according to the specific scene in the visual sensor network. Numerous experiments are conducted in both indoor and outdoor specific scenes. The experimental results show that our method is effective.
基金Dr.Deepak Dahiya would like to thank Deanship of Scientific Research at Majmaah University for supporting his work under Project No.(R-2022-96)。
文摘As a component of Wireless Sensor Network(WSN),Visual-WSN(VWSN)utilizes cameras to obtain relevant data including visual recordings and static images.Data from the camera is sent to energy efficient sink to extract key-information out of it.VWSN applications range from health care monitoring to military surveillance.In a network with VWSN,there are multiple challenges to move high volume data from a source location to a target and the key challenges include energy,memory and I/O resources.In this case,Mobile Sinks(MS)can be employed for data collection which not only collects information from particular chosen nodes called Cluster Head(CH),it also collects data from nearby nodes as well.The innovation of our work is to intelligently decide on a particular node as CH whose selection criteria would directly have an impact on QoS parameters of the system.However,making an appropriate choice during CH selection is a daunting task as the dynamic and mobile nature of MSs has to be taken into account.We propose Genetic Machine Learning based Fuzzy system for clustering which has the potential to simulate human cognitive behavior to observe,learn and understand things from manual perspective.Proposed architecture is designed based on Mamdani’s fuzzy model.Following parameters are derived based on the model residual energy,node centrality,distance between the sink and current position,node centrality,node density,node history,and mobility of sink as input variables for decision making in CH selection.The inputs received have a direct impact on the Fuzzy logic rules mechanism which in turn affects the accuracy of VWSN.The proposed work creates a mechanism to learn the fuzzy rules using Genetic Algorithm(GA)and to optimize the fuzzy rules base in order to eliminate irrelevant and repetitive rules.Genetic algorithmbased machine learning optimizes the interpretability aspect of fuzzy system.Simulation results are obtained using MATLAB.The result shows that the classification accuracy increase along with minimizing fuzzy rules count and thus it can be inferred that the suggested methodology has a better protracted lifetime in contrast with Low Energy Adaptive Clustering Hierarchy(LEACH)and LEACHExpected Residual Energy(LEACH-ERE).
文摘First,the constitution of traditional visual sensor is presented.The linear camera model is introduced and the transform matrix between the image coordinate system and the world coordinate system is established.The basic principle of camera calibration is expatiated based on the linear camera model. On the basis of a detailed analysis of camera model,a new-style visual sensor for measurement is advanced.It can realize the real time control of the zoom of camera lens by step motor according to the size of objects.Moreover,re-calibration could be avoided and the transform matrix can be acquired by calculating,which can greatly simplify camera calibration process and save the time. Clearer images are gained,so the measurement system precision could be greatly improved.The basic structure of the visual sensor zoom is introduced,including the constitute mode and the movement rule of the fixed former part,zoom part,compensatory part and the fixed latter port.The realization method of zoom controlled by step motor is introduced. Finally,the constitution of the new-style visual sensor is introduced,including hardware and software.The hardware system is composed by manual zoom,CCD camera,image card,gearing,step motor,step motor driver and computer.The realization of software is introduced,including the composed module of software and the workflow of measurement system in the form of structured block diagram.
文摘Wireless visual sensor network (VSN) can be said to be a special class of wireless sensor network (WSN) with smart-cameras. Due to its visual sensing capability, it has become an effective tool for applications such as large area surveillance, environmental monitoring and objects tracking. Different from a conventional WSN, VSN typically includes relatively expensive camera sensors, enhanced flash memory and a powerful CPU. While energy consumption is dominated primarily by data transmission and reception, VSN consumes extra power onimage sensing, processing and storing operations. The well-known energy-hole problem of WSNs has a drastic impact on the lifetime of VSN, because of the additional energy consumption of a VSN. Most prior research on VSN energy issues are primarily focusedon a single device or a given specific scenario. In this paper, we propose a novel optimal two-tier deployment strategy for a large scale VSN. Our two-tier VSN architecture includes tier-1 sensing network with visual sensor nodes (VNs) and tier-2 network having only relay nodes (RNs). While sensing network mainly performs image data collection, relay network only for wards image data packets to the central sink node. We use uniform random distribution of VNs to minimize the cost of VSN and RNs are deployed following two dimensional Gaussian distribution so as to avoid energy-hole problem. Algorithms are also introduced that optimizes deployment parameters and are shown to enhance the lifetime of the VSN in a cost effective manner.
基金supported by the National Natural Science Foundationof China(61100207)the National Key Technology Research and Development Program of the Ministry of Science and Technology of China(2014BAK14B03)+1 种基金the Fundamental Research Funds for the Central Universities(2013PT132013XZ12)
文摘Most sensors or cameras discussed in the sensor network community are usually 3D homogeneous, even though their2 D coverage areas in the ground plane are heterogeneous. Meanwhile, observed objects of camera networks are usually simplified as 2D points in previous literature. However in actual application scenes, not only cameras are always heterogeneous with different height and action radiuses, but also the observed objects are with 3D features（i.e., height）. This paper presents a sensor planning formulation addressing the efficiency enhancement of visual tracking in 3D heterogeneous camera networks that track and detect people traversing a region. The problem of sensor planning consists of three issues：（i） how to model the 3D heterogeneous cameras;（ii） how to rank the visibility, which ensures that the object of interest is visible in a camera＇s field of view;（iii） how to reconfigure the 3D viewing orientations of the cameras. This paper studies the geometric properties of 3D heterogeneous camera networks and addresses an evaluation formulation to rank the visibility of observed objects. Then a sensor planning method is proposed to improve the efficiency of visual tracking. Finally, the numerical results show that the proposed method can improve the tracking performance of the system compared to the conventional strategies.
文摘智能化焊接在推进“工业强基”工程、支撑国家建设及国防安全中起到重要作用,从重大装备到精细结构,焊接都是不可或缺的关键技术,而机器人作为智能化焊接的重要载体,推动“以机器代替人,以机器解放人”的过程中将发挥重要作用。文中从焊接制造全流程的场景建模、焊接过程形性原位感知、自适应调控、工艺知识构建等关键技术出发,重点阐述了焊接机器人的“免示教”编程环境感知、点云配准、焊缝轨迹规划和焊道自适应编排等共性技术的研究现状,以智能化焊接制造过程多源信息监测及控制系统为例,提出了基于IIOT-MAS(industrial internet of things-multi-agent system)焊接制造系统分层结构模型,介绍了焊接多模态信息感知、融合及工艺知识建模等共性科学问题,并介绍了工程机械部件焊接现场感知数据在线学习和模型-数据双驱动的焊接质量评价模型典型案例,探讨了机器人焊接智能化的发展趋势和所面临的挑战。
文摘水下同步定位与建图(Simultaneous Localization and Mapping,SLAM)技术使水下机器人能在未知水下环境中同时进行自我定位和环境地图构建,对海洋学研究、海底资源勘探等领域具有重要意义。本文综述了水下视觉SLAM技术最新研究进展、挑战与解决方案及未来研究方向,梳理了水下视觉SLAM的关键理论。水下环境的复杂性,如光线衰减、散射和水流影响,为水下SLAM的研究带来挑战。本文分析了水下视觉SLAM的最新研究进展,包括多传感器融合、深度学习技术及优化算法的应用,这些技术提高了水下SLAM系统的鲁棒性和精度。同时,本文还探讨了水下SLAM技术面临的主要挑战,并提出了可能的解决方案,如提高传感器数据的准确性、增强数据融合算法的实时性和鲁棒性、改进特征提取与匹配方法,以及提升定位与建图算法的精度和稳定性。最后,本文对水下SLAM的未来研究方向进行了展望,包括新型传感器技术、人工智能技术的应用和水下多机器人协同SLAM的发展,旨在提供该领域科研与技术发展的整体视角。
文摘目前水果采摘存在劳动力短缺、采摘效率低和作业环境复杂等问题,亟须发展具备高精度感知与自主作业能力的智能化采摘装备,以全面提升果实采摘的效率和质量。传感器技术在水果采摘机器人中的应用包括路径规划、果实识别、定位及抓取控制等关键任务环节。针对非结构化果园环境,视觉、触觉与激光传感器的协同应用可实现目标识别、位置感知与避障控制,显著提升了采摘机器人对复杂环境的适应能力与作业精度,但是现有传感器仍然存在一些技术短板,如视觉传感器易受阳光干扰、枝叶遮挡和果实密集分布等因素影响,导致目标检测困难;触觉传感器易受温湿度影响,难以量化复杂的力学反馈,因而细微抓取力控制困难;由于非结构化环境下的路径优化困难,且激光传感器成本高昂,限制了其大规模应用。同时,单一传感器存在感知维度单一、环境适应性不足和果实特征感知不足等局限,难以适应非结构化果园环境。为此,针对多传感器融合技术面临的数据异构性、时序同步性和计算复杂性等挑战,对传感器技术在水果采摘机器人的未来应用进行了展望,指出融合红外、紫外等多波段成像技术和高动态范围(high dynamic range imaging,HDR)成像技术,柔性电子皮肤结合仿生结构设计的多传感器融合技术有望得到广泛应用。
基金supported by National Natural Science Foundation of China (Nos. 60805038 and 60725309)Beijing Natural Science Foundation (No. 4082032)
文摘The relative pose between inertial and visual sensors equipped in autonomous robots is calibrated in two steps. In the first step, the sensing system is moved along a line, the orientations in the relative pose are computed from at least five corresponding points in the two images captured before and after the movement. In the second step, the translation parameters in the relative pose are obtained with at least two corresponding points in the two images captured before and after one step motion. Experiments are conducted to verify the effectiveness of the proposed method.
