增加翻译内容.

Signed-off-by: ithink.chan <chenyang@autoai.com>

Algorithm/AI/Deep_Learning_Haar_Cascade_Explained.md

@@ -77,6 +77,14 @@ Haar 级联分类器最著名的应用是检测图像中的人脸或身体，但
 
 “The cascade classifier consists of a collection of stages, where each stage is an ensemble of weak learners. The weak learners are simple classifiers called decision stumps. Each stage is trained using a technique called boosting. Boosting provides the ability to train a highly accurate classifier by taking a weighted average of the decisions made by the weak learners.”
 
+级联分类器由一系列小的阶段组成，每个阶段就是一个弱学习过程。弱学习过程是一个被称作单层分类器的简单分类器。每个阶段由 boosting 算法进行训练。通过对每个阶段的决策进行加权，Boosting 提供了训练高准确度分类器的方法。
+
+“Each stage of the classifier labels the region defined by the current location of the sliding window as either positive or negative. Positive indicates that an object was found and negative indicates no objects were found. If the label is negative, the classification of this region is complete, and the detector slides the window to the next location. If the label is positive, the classifier passes the region to the next stage. The detector reports an object found at the current window location when the final stage classifies the region as positive.”
+
+每个阶段的分类器对滑动窗口的当前区域打标签（positive 或 negative）。Positive 意味着检测到了目标，negative 意味着没有检测到目标。如果没有检测到目标则完成当前区域的检测，滑动窗口移动到下一个区域。如果检测结果 positive，则分类器将当前区域传递给下一个阶段继续检测。当所有检测阶段都返回 positive，那么检测器报告在当前位置发现了目标物。
+
+“The stages are designed to reject negative samples as fast as possible. The assumption is that the vast majority of windows do not contain the object of interest. Conversely, true positives are rare and worth taking the time to verify.”
+
 ## 标注
 
 [1] Haar