这片论文描述了动态物体的特征跟踪，用到了15个框架。拥有很强的适应性和跟踪能力。作为人脸识别，模式识别，动态跟踪的开发人员，有很好的参考价值。用c++编写，如果用OpenCV更好-This paper describes a visual object detection framework that is capable of processing

images extremely rapidly while achieving high detection rates. There are three

key contributions. The first is the introduction of a new image representation called the

“Integral Image” which allows the features used by our detector to be computed very

quickly. The second is a learning algorithm, based on AdaBoost, which selects a small

number of critical visual features and yields extremely efficient classifiers [4]. The

third contribution is a method for combining classifiers in a “cascade” which allows

background regions of the image to be quickly discarded while spending more computation

on promising object-like regions. A set of experiments in the domain of face

detection are presented. The system yields face detection performance comparable to

the best previous systems [16, 11, 14, 10, 1]. Implemented on a conventional desktop,

face detection proceeds at 15 frames per second