Rapid Object Detection using a Boosted Cascade of Simple 采用升压级联简单的快速目标检测.pptVIP

Rapid Object Detection using a Boosted Cascade of Simple Features Original Author Paul Viola Michael Jones In: Proc. Conf. Computer Vision and Pattern Recognition. Volume 1., Kauai, HI, USA (2001) 511–518 Outline Introduction The Boost algorithm for classifier learning Feature Selection Weak learner constructor The strong classifier A tremendously difficult problem Result Conclusion What had we done? A machine learning approach for visual object detection Capable of processing images extremely rapidly Achieving high detection rates Three key contributions A new image representation ? Integral Image A learning algorithm( Based on AdaBoost[5]) A combining classifiers method ? cascade classifiers A demonstration on face detection A frontal face detection system The detector run at 15 frames per second without resorting to image differencing or skin color detection The broad practical applications for a extremely fast face detector User Interface, Image Databases, Teleconferencing The system can be implemented on a small low power devices. Training process for classifier The attentional operator is trained to detect examples of a particular class a supervised training process Cascaded detection process The sub-windows are processed by a sequence of classifiers Our object detection framework Feature Selection The simple features are used Three kinds of features Feature Selection Integral Image Calculating any rectangle sum with integral image Learning Classification Functions The Boost algorithm for classifier learning Weak learner constructor 圖示解說 Training the weak learner 圖解說明 AdaBoosting Place the most weight on the examples must often misclassified by the preceding weak rules Forcing the base learner to focus its attention on the “hardest” examples The Boost algorithm for classifier learning The Big Picture on testing process A tremendously difficult problem How to determ