1. A classifier cascade object detection system, comprising a non transitory memory and one or more processors, the non transitory memory having executable instructions encoded thereon, such that upon execution of the instructions by the one or more processors, the one or more processors perform operations of: inputting an image patch into parallel feature generation modules, each of the feature generation modules operable for extracting different features from the image patch; providing the different features to a classifier cascade, the classifier cascade having a series of different classifiers; and executing the classifier cascade by progressively evaluating, in each classifier in the classifier cascade, one or more of the different features to produce a response, if each classifier produces a response that exceeds a predefined threshold then the image patch is classified as a target object, and if the response from any of the classifiers in the classifier cascade does not exceed the predefined threshold, then the image patch is classified as a non-target object; wherein the classifier cascade is trained by performing operations of: loading parameters of the image patch, including window position (x,y) with respect to a larger image and window height (h); loading a cascade template file, the cascade template file defining a number of classifier stages in the classifier cascade, one or more feature types, a classifier type to use in each classifier stage, an aspect ratio of the image patch, and a height of an image storage container which all image patches are re-sampled to; computing features based on features specified in the cascade template file; compiling the features for a classifier trainer, the classifier trainer generating a trained classifier; generating a Receiver-Operating-Characteristics (ROC) curve, which includes false-alarm rate and true-detection rate pairs (FAR,TDR) for a given stage threshold; and tuning the stage threshold.