基于Gentle AdaBoost算法的牛体检测

Gentle Ada Boost based cow detection

针对目标在形状、外观和光照条件发生较大变化时产生的检测率低的问题,以牛体检测为例提出了基于Gentle AdaBoost算法的牛体检测。利用bag of features(BOF)的思想创建特征词典,然后通过词典对牛体目标进行特征提取,最后通过Gentle AdaBoost算法对训练集的BOF特征向量进行训练分类,获得目标对象和场景的分类模型。实验结果表明,该算法训练的检测器在牛体目标存在光照不均匀、形变时均可实现可靠的检测。

To solve the problem of low detection rate when targets occur great changes in shape, appearance and light illumi- nation conditions, taking cow detection for example, this paper proposed a Gentle AdaBoost algorithm based cow body detec- tion method. Firstly, it created feature dictionary by improved BOF. Then the cow target was extracted by the dictionary. Fi- nally, it processed training and classifying for the feature vectors of the data set to obtain the classification model of cow object and scene. Experiments show that the detectors trained by the proposed algorithm can achieve reliable detection results even in non-uniform and deformed situations.