Adaptive threshold for object detection

摘要

Systems and methods for developing and using adaptive threshold values for different input images for object detection are disclosed. In embodiments, detector response histogram-based systems and methods train models for predicting optimal threshold values for different images. In embodiments, when training the model, an optimal threshold value for an image is defined as the value that maximizes the reduction of false positive image patches while preserving as many true positive image patches as possible. Once trained, the model may be used to set different threshold values for different images by inputting a detector response histogram for the image patches of an image into the model to determine a threshold value for detection.

主权项

1. A computer-implemented method to detect an object in an input image, the method comprising:
submitting said input image to a classifier for initial object detection, said classifier being already trained to detect said object, said classifier identifying multiple image regions within said input image and assigning each image region a detector response value indicative of a likelihood of the object, being within the image region, each image region whose detector response value is not less than a first threshold value being classified as containing the object and being designated a candidate patch; collecting candidate patches to form a set of candidate patches from the input image, each candidate patch having its corresponding detector response value associated with it; using a trained model to determine a second threshold value based on the detector response values candidate of the candidate patches in the set of candidate patches; re-classifying all the candidate patches using the second threshold value, wherein each candidate patch whose associated detector response value is not less than the second threshold value is classified as containing the object, and each candidate patch whose associated detector response value is less than the second threshold value is re-classified as not containing the object.