针对传统关联波门在杂波环境下的机动目标跟踪中容易出现丢失目标的问题,提出了一种基于模糊推理的自适应关联波门设计方法。该方法在概率数据关联(Probabilistic Data Association,PDA)算法的基础上进行关联波门的设计,建立机动目标跟...针对传统关联波门在杂波环境下的机动目标跟踪中容易出现丢失目标的问题,提出了一种基于模糊推理的自适应关联波门设计方法。该方法在概率数据关联(Probabilistic Data Association,PDA)算法的基础上进行关联波门的设计,建立机动目标跟踪场景中最优波门门限的数学模型,运用模糊推理算法,根据目标的机动性和目标所处环境杂波密度自适应调整波门门限。仿真结果表明,该方法相较于传统关联波门,目标的失跟率降低了21%,而且提高了目标的跟踪精度。展开更多
An adaptive topology learning approach is proposed to learn the topology of a practical camera network in an unsupervised way. The nodes are modeled by the Gaussian mixture model. The connectivity between nodes is jud...An adaptive topology learning approach is proposed to learn the topology of a practical camera network in an unsupervised way. The nodes are modeled by the Gaussian mixture model. The connectivity between nodes is judged by their cross-correlation function, which is also used to calculate their transition time distribution. The mutual information of the connected node pair is employed for transition probability calculation. A false link eliminating approach is proposed, along with a topology updating strategy to improve the learned topology. A real monitoring system with five disjoint cameras is built for experiments. Comparative results with traditional methods show that the proposed method is more accurate in topology learning and is more robust to environmental changes.展开更多
In this paper, we propose an enhanced associative classification method by integrating the dynamic property in the process of associative classification. In the proposed method, we employ a support vector machine（SVM...In this paper, we propose an enhanced associative classification method by integrating the dynamic property in the process of associative classification. In the proposed method, we employ a support vector machine（SVM） based method to refine the discovered emerging ~equent patterns for classification rule extension for class label prediction. The empirical study shows that our method can be used to classify increasing resources efficiently and effectively.展开更多
文摘多假设跟踪（multiple hypothesis tracking,MHT）方法是一种在多个扫描上评价关联假设并由此做出决策的贝叶斯型关联跟踪方法,此方法能够在信噪比低10-100倍的状况下获得与单扫描方法相当的性能,但同时会带来相当大的计算量。本文研究了面向航迹MHT中的关键算法,包括航迹得分计算与航迹树的生成、将航迹聚类和假设生成建模为图论问题并求解、N扫描回溯剪枝等,特别关注了这些算法过程的实现;提出了一种关联深度自适应（adaptive association depth,AAD）方法,使关联深度随关联场景的复杂程度自适应变化;仿真研究了本文提出的AAD-MHT跟踪密集目标的性能,结果和分析表明,与深度值固定为6的MHT相比,最大深度为6的AAD-MHT既能保证性能又有效降低了计算量。
文摘针对传统关联波门在杂波环境下的机动目标跟踪中容易出现丢失目标的问题,提出了一种基于模糊推理的自适应关联波门设计方法。该方法在概率数据关联(Probabilistic Data Association,PDA)算法的基础上进行关联波门的设计,建立机动目标跟踪场景中最优波门门限的数学模型,运用模糊推理算法,根据目标的机动性和目标所处环境杂波密度自适应调整波门门限。仿真结果表明,该方法相较于传统关联波门,目标的失跟率降低了21%,而且提高了目标的跟踪精度。
基金The National Natural Science Foundation of China(No.60972001)the Science and Technology Plan of Suzhou City(No.SS201223)
文摘An adaptive topology learning approach is proposed to learn the topology of a practical camera network in an unsupervised way. The nodes are modeled by the Gaussian mixture model. The connectivity between nodes is judged by their cross-correlation function, which is also used to calculate their transition time distribution. The mutual information of the connected node pair is employed for transition probability calculation. A false link eliminating approach is proposed, along with a topology updating strategy to improve the learned topology. A real monitoring system with five disjoint cameras is built for experiments. Comparative results with traditional methods show that the proposed method is more accurate in topology learning and is more robust to environmental changes.
基金Supported by the National High Technology Research and Development Program of China （No. 2007AA01Z132） the National Natural Science Foundation of China （No.60775035, 60933004, 60970088, 60903141）+1 种基金 the National Basic Research Priorities Programme （No. 2007CB311004） the National Science and Technology Support Plan （No.2006BAC08B06）.
文摘In this paper, we propose an enhanced associative classification method by integrating the dynamic property in the process of associative classification. In the proposed method, we employ a support vector machine（SVM） based method to refine the discovered emerging ~equent patterns for classification rule extension for class label prediction. The empirical study shows that our method can be used to classify increasing resources efficiently and effectively.
