4. The method of claim 1 , wherein the evaluating further comprises: combining each decision from all of the classifiers in the plurality of classifiers into a unified decision; determining that the unified decision indicates that the area of the at least one image comprises an instance of the desired object type; if another layer exists in the plurality of increasingly accurate layers, evaluating the current sliding window at another layer in the plurality of layers; and if another layer fails to exist in the plurality of increasingly accurate layers, indicating that the current sliding window represents an instance of the desired object type.
1. A method for detecting objects in a digital image, the method comprising: receiving at least one image representing at least one frame of a video sequence comprising zero or more objects of at least one desired object type; placing a sliding window of different window sizes at different locations in the at least one image; applying, for each window size and each location, a cascaded classifier comprising a plurality of increasingly accurate layers, each layer comprising a plurality of classifiers; evaluating, at each layer in the plurality of increasingly accurate layers, an area of the at least one image within a current sliding window using one or more weak classifiers in the plurality of classifiers based on at least one of Haar features and Histograms of Oriented Gradients (HOG) features, wherein an output of each weak classifier is a weak decision as to whether the area of the at least one image within the current sliding window comprises an instance of an object of the desired object type; identifying, based on the evaluating, a location within the image of the zero or more objects associated with the desired object type; and training each weak classifier in the plurality of classifiers based on Haar features and HOG features, wherein a selection of a subsequent weak classifier during the training is based on the subsequent weak classifier that provides a strongest separation between desired object types than other available weak classifiers independent of the subsequent weak classifier being associated with one of a Haar feature and a HOG feature.