This paper develops a fast filtering algorithm based on vibration systems theory and neural information exchange approach. The characters, including the derivation process and parameter analysis, are discussed and the...This paper develops a fast filtering algorithm based on vibration systems theory and neural information exchange approach. The characters, including the derivation process and parameter analysis, are discussed and the feasibility and the effectiveness are testified by the filtering performance compared with various filtering methods, such as the fast wavelet transform algorithm, the particle filtering method and our previously developed single degree of freedom vibration system filtering algorithm, according to simulation and practical approaches. Meanwhile, the comparisons indicate that a significant advantage of the proposed fast filtering algorithm is its extremely fast filtering speed with good filtering perfi^rmance. Further, the developed fast filtering algorithm is applied to the navigation and positioning system of the micro motion robot, which is a high real-time requirement for the signals preprocessing. Then, the preprocessing data is used to estimate the heading angle error and the attitude angle error of the micro motion robot. The estimation experiments illustrate the high practicality of the proposed fast filtering algorithm.展开更多
Abnormal behavior detection is challenging and one of the growing research areas in computer vision.The main aim of this research work is to focus on panic and escape behavior detections that occur during unexpected/u...Abnormal behavior detection is challenging and one of the growing research areas in computer vision.The main aim of this research work is to focus on panic and escape behavior detections that occur during unexpected/uncertain events.In this work,Pyramidal Lucas Kanade algorithm is optimized using EME-HOs to achieve the objective.First stage,OPLKT-EMEHOs algorithm is used to generate the opticalflow from MIIs.Second stage,the MIIs opticalflow is applied as input to 3 layer CNN for detect the abnormal crowd behavior.University of Minnesota(UMN)dataset is used to evaluate the proposed system.The experi-mental result shows that the proposed method provides better classification accu-racy by comparing with the existing methods.Proposed method provides 95.78%of precision,90.67%of recall,93.09%of f-measure and accuracy with 91.67%.展开更多
Unmanned weapons have great potential to be widely used in future wars.The gaze-based aiming technology can be applied to control pan-tilt weapon systems remotely with high precision and efficiency.Gaze direction is r...Unmanned weapons have great potential to be widely used in future wars.The gaze-based aiming technology can be applied to control pan-tilt weapon systems remotely with high precision and efficiency.Gaze direction is related to head motion,which is a combination of head and eye movements.In this paper,a head motion detection method is proposed,which is based on the fusion of inertial and vision information.The inertial sensors can measure rotation in high-frequency with good performance,while vision sensors are able to eliminate drifts.By combining the characteristics of both sensors,the proposed approach achieves the effect of highfrequency,real-time,and drift-free head motion detection.The experiments show that our method can smooth the outputs,constrain drifts of inertial measurements,and achieve high detection accuracy.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.60901074,51075092,61005076,and 61175107)the National High Technology Research and Development Program of China(Grant No.2007AA042105)the Natural Science Foundation of Heilongjiang Province,China(Grant No.E200903)
文摘This paper develops a fast filtering algorithm based on vibration systems theory and neural information exchange approach. The characters, including the derivation process and parameter analysis, are discussed and the feasibility and the effectiveness are testified by the filtering performance compared with various filtering methods, such as the fast wavelet transform algorithm, the particle filtering method and our previously developed single degree of freedom vibration system filtering algorithm, according to simulation and practical approaches. Meanwhile, the comparisons indicate that a significant advantage of the proposed fast filtering algorithm is its extremely fast filtering speed with good filtering perfi^rmance. Further, the developed fast filtering algorithm is applied to the navigation and positioning system of the micro motion robot, which is a high real-time requirement for the signals preprocessing. Then, the preprocessing data is used to estimate the heading angle error and the attitude angle error of the micro motion robot. The estimation experiments illustrate the high practicality of the proposed fast filtering algorithm.
文摘Abnormal behavior detection is challenging and one of the growing research areas in computer vision.The main aim of this research work is to focus on panic and escape behavior detections that occur during unexpected/uncertain events.In this work,Pyramidal Lucas Kanade algorithm is optimized using EME-HOs to achieve the objective.First stage,OPLKT-EMEHOs algorithm is used to generate the opticalflow from MIIs.Second stage,the MIIs opticalflow is applied as input to 3 layer CNN for detect the abnormal crowd behavior.University of Minnesota(UMN)dataset is used to evaluate the proposed system.The experi-mental result shows that the proposed method provides better classification accu-racy by comparing with the existing methods.Proposed method provides 95.78%of precision,90.67%of recall,93.09%of f-measure and accuracy with 91.67%.
基金Supported by Defense Industrial Technology Development Program(JCKY2017602C016)。
文摘Unmanned weapons have great potential to be widely used in future wars.The gaze-based aiming technology can be applied to control pan-tilt weapon systems remotely with high precision and efficiency.Gaze direction is related to head motion,which is a combination of head and eye movements.In this paper,a head motion detection method is proposed,which is based on the fusion of inertial and vision information.The inertial sensors can measure rotation in high-frequency with good performance,while vision sensors are able to eliminate drifts.By combining the characteristics of both sensors,the proposed approach achieves the effect of highfrequency,real-time,and drift-free head motion detection.The experiments show that our method can smooth the outputs,constrain drifts of inertial measurements,and achieve high detection accuracy.
