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Multi moving target detection
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Application background The detection and tracking of moving target is a technology which integrates computer vision, video image processing, pattern recognition and automatic control.Knowledge of the system and other related fields [2]. The detection and tracking of moving target is an important research direction of video technology, and its application is tenWidely. In traffic monitoring, security monitoring, military guidance, visual navigation, and video coding are involved.At present, the detection and tracking of moving target has achieved many results, and there are new technologies and new algorithms emerging.. But,In the actual environment, due to the complex nature of the environment (light, climate, etc.), the target of high mobility, interference with the targetThe detection and tracking of the target is inaccurate and the tracking efficiency is not high. Therefore, the research of the moving target detection and tracking algorithm is improved.Have very
richardlyy
2016-08-23
0
1
Data mining algorithm C4.5 decision trees generate
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&Decision tree algorithm is a kind of supervised learning, that is to say, a certain category and data set will be given in advance. Through learning, we can determine the class of the incoming data. Of course, many clustering algorithms are unsupervised learning, we will discuss later. As the name suggests, a decision tree is a tree like data structure, which can be either a multi tree or a binary tree. In fact, decision tree is constructed based on greedy strategy, and the optimal attribute is selected to split each time. The commonly used decision tree algorithms are ID3 and C4.5. In fact, these two algorithms are essentially the same, and they are found independently almost at the same time. ID3 the purpose of this algorithm is to reduce the depth of the tree. But the study of leaf number is ignored. C4.5 algorithm is improved on the basis of ID3. It is suitable for both classification and regression problems. It improves the missing value processing of prediction variables, pruning technology and derivation rules. Sometimes the decision tree will also have pruning considerations, mainly from the perspective of performance, noise and efficiency. &The application of the algorithm
richardlyy
2016-08-23
0
1
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