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
With notably few exceptions, the existing satellite mission operations cannot provide the ability of schedulability prediction, including the latest satellite planning service （SPS） standard–Sensor Planning Service...With notably few exceptions, the existing satellite mission operations cannot provide the ability of schedulability prediction, including the latest satellite planning service （SPS） standard–Sensor Planning Service Interface Standard 2.0 Earth Observation Satellite Tasking Extension （EO SPS） approved by Open Geospatial Consortium （OGC）. The requestor can do nothing but waiting for the results of time consuming batch scheduling. It is often too late to adjust the request when receiving scheduling failures. A supervised learning algorithm based on robust decision tree and bagging support vector machine （Bagging SVM） is proposed to solve the problem above. The Bagging SVM is applied to improve the accuracy of classification and robust decision tree is utilized to reduce the error mean and error variation. The simulations and analysis show that a prediction action can be accomplished in near real-time with high accuracy. This means the decision makers can maximize the probability of successful scheduling through changing request parameters or take action to accommodate the scheduling failures in time.展开更多
This paper presents a new method for behavior fusion control of a mobile robot in uncertain environments. Using behavior fusion by fuzzy logic, a mobile robot is able to directly execute its motion according to range ...This paper presents a new method for behavior fusion control of a mobile robot in uncertain environments. Using behavior fusion by fuzzy logic, a mobile robot is able to directly execute its motion according to range information about environments, acquired by ultrasonic sensorst without the need for trajectory planning. Based on low-level behavior control, an efficient strategy for integrating high-level global planning for robot motion can be formulated, since,in most applications, some information on environments is prior knowledge. Aglobal planner, therefore, only needs to generate some subgoal positions ratherthan exact geometric paths. Because such subgoals can be easily removed from or added into the planner, this. strategy reduces computational time for globalplanning and is flekible for replanning in dynamic environments. Simulationresults demonstrate that the proposed strategy can be applied to robot motion in complex and dynamic environments.展开更多
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.
基金the National Natural Science Foundation of China(Nos.61174159 and 61101184)
文摘With notably few exceptions, the existing satellite mission operations cannot provide the ability of schedulability prediction, including the latest satellite planning service （SPS） standard–Sensor Planning Service Interface Standard 2.0 Earth Observation Satellite Tasking Extension （EO SPS） approved by Open Geospatial Consortium （OGC）. The requestor can do nothing but waiting for the results of time consuming batch scheduling. It is often too late to adjust the request when receiving scheduling failures. A supervised learning algorithm based on robust decision tree and bagging support vector machine （Bagging SVM） is proposed to solve the problem above. The Bagging SVM is applied to improve the accuracy of classification and robust decision tree is utilized to reduce the error mean and error variation. The simulations and analysis show that a prediction action can be accomplished in near real-time with high accuracy. This means the decision makers can maximize the probability of successful scheduling through changing request parameters or take action to accommodate the scheduling failures in time.
文摘This paper presents a new method for behavior fusion control of a mobile robot in uncertain environments. Using behavior fusion by fuzzy logic, a mobile robot is able to directly execute its motion according to range information about environments, acquired by ultrasonic sensorst without the need for trajectory planning. Based on low-level behavior control, an efficient strategy for integrating high-level global planning for robot motion can be formulated, since,in most applications, some information on environments is prior knowledge. Aglobal planner, therefore, only needs to generate some subgoal positions ratherthan exact geometric paths. Because such subgoals can be easily removed from or added into the planner, this. strategy reduces computational time for globalplanning and is flekible for replanning in dynamic environments. Simulationresults demonstrate that the proposed strategy can be applied to robot motion in complex and dynamic environments.
