Object detection using cascaded convolutional neural networks

摘要

Different candidate windows in an image are identified, such as by sliding a rectangular or other geometric shape of different sizes over an image to identify portions of the image (groups of pixels in the image). The candidate windows are analyzed by a set of convolutional neural networks, which are cascaded so that the input of one convolutional neural network layer is based on the input of another convolutional neural network layer. Each convolutional neural network layer drops or rejects one or more candidate windows that the convolutional neural network layer determines does not include an object (e.g., a face). The candidate windows that are identified as including an object (e.g., a face) are analyzed by another one of the convolutional neural network layers. The candidate windows identified by the last of the convolutional neural network layers are the indications of the objects (e.g., faces) in the image.

主权项

1. A method comprising:
identifying multiple candidate windows in an image, each candidate window including a group of pixels of the image, the multiple candidate windows including overlapping candidate windows; identifying one or more of the multiple candidate windows that include an object, the identifying including analyzing the multiple candidate windows using cascaded convolutional neural networks, the cascaded convolutional neural networks including multiple cascade layers, each cascade layer comprising a convolutional neural network, the multiple cascade layers including a first cascade layer that analyzes the identified multiple candidate windows, a second cascade layer that analyzes ones of the multiple candidate windows identified by the first cascade layer as including an object, and a third cascade layer that analyzes ones of the multiple candidate windows identified by the second cascade layer as including an object, the third cascade layer analyzing higher resolution versions of the ones of the multiple candidate windows identified by the second cascade layer as including an object than versions analyzed by the first cascade layer; and outputting, as an indication of one or more objects in the image, an indication of one or more of the multiple candidate windows identified by the third cascade layer as including an object.