Object detection method based on feature redundancy elimination AdaBoost classifier
A technology of object detection and classifier, applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve the problems of large calculation time, slow learning speed, large difference, etc., and achieve simple, high efficiency, and high real-time The effect of sex and processing speed
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[0030] The following is a further description of the method proposed in this paper in combination with the haar feature detection process of the object:
[0031] The present invention uses the object detection method of weight-based redundant feature reduction AdaBoost classifier, such as figure 1 As shown, it specifically includes the following steps:
[0032] Step 1, classifier training, such as figure 2 shown.
[0033] (1) Input: a feature set F={f 1 ,..., f K} and S={(x 1 ,y 1 ),..., (x N ,y N )} This is a labeled training set where y i ={0, 1} respectively correspond to positive samples and negative samples, a combined classifier h and a given cycle number T, elimination coefficient λ, and association threshold γ.
[0034] (2) Initialization: Dataset Initialize training sample weights: when y i = 0, when y j = 1, Among them, m and l represent the number of negative samples and positive samples respectively, and the feature weight D: d 1,i = 1.0, feature...
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