Deformable parts-based object recognition for Open CV

During his summer internship at Willow Garage, Hilton Bristow, a PhD. student from the Queensland University of Technology, Australia, implemented a deformable parts-based object recognition method.  There are many perception situations when only monocular (single camera) visual data is available, and in such situations, robust, efficient object detection techniques are desired. 

Object recognition using mixtures of deformable parts is a state-of-the-art technique for monocular object recognition.  Hilton ported an existing method by Deva Ramanan from Matlab to C++ to improve the computational performance and make it more accessible to the computer vision and robotics communities alike.  In doing so, he recognized that depth data (such as with the kinnect sensor) could be leveraged to prune the object search space, and disambiguate multiple superimposed object candidates.  The result was an object detection framework capable of detecting human bodies at 1-2 frames per second (fps) and simpler objects at 5-10 fps.

Give the code a shot! You can find it in the wg-perception repository on GitHub, along with a number of pre-trained models and bindings to ROS and the ECTO synchronous vision pipeline. For more information check out the video.