Detecting objects in images

摘要

Techniques for detecting the location of an object of interest in a visual image are presented. A detector component extracts Histogram of Gradient (HOG) features from grid regions associated with the visual image. A trained linear filter model uses a classifier to facilitate differentiating between positive and negative instances of the object in grid regions based on HOG features. A classifier component detects the K top-scoring activations of filters associated with the visual image. The classifier component detects the location of the object in the visual image based on a generalized Hough transform, given filter locations associated with the visual image. The classifier component projects the object location given filter activations and clusters the filter activations into respective clusters. The classifier component classifies whether a cluster is associated with the object based on the weighted sum of the activation scores of filters within the cluster and object detection criteria.

主权项

1. A system, comprising:
at least one memory that stores computer executable components; and at least one processor that executes the following computer executable components stored in the at least one memory:
a detector component that extracts Histogram of Gradient (HOG) features from grid regions associated with a visual image to facilitate detection of a location of an object of interest in the visual image; anda classifier component that uses a trained linear filter model to determine whether the visual image potentially contains the object of interest based at least in part on the HOG features, wherein the classifier component clusters a subset of filter activations associated with the trained filter model to generate a cluster of filter activations that identifies a potential location of the object of interest in the visual image, and wherein the classifier component determines whether the cluster of filter activations is associated with the object of interest in the visual image based at least in part on a Hough transform and a weighted sum of filter activation scores of the subset of filter activations within the cluster of filter activations.