Adaptive Bandwidth Mean Shift Object Detection

Abstract

In this paper, a novel adaptive bandwidth mean shift algorithm toward 2D object detection (ABMSOD) is proposed. It can not only identify whether an object of certain classes exists or not, but also get the scale and orientation besides position very fast. The feature histogram weighted by a kernel with adaptive bandwidth is used for representing the target object model and the candidate object model. Features such as color, texture, gradient and so on can be used. A single piece of image is enough to build a model by calculating the weighted feature histogram of the object in the image. There is no exhaustive training. The similarity of the target model and the candidate model is measured by the Bhattacharyya coefficient. After gathering the models of targets, the algorithm can be used for object detection. In the first step, the algorithm searches the whole image to find the rough positions of possible candidate objects. If the similarities are all below a certain threshold, it reports no object existence. If the similarities are above the threshold, the second step or the adaptive bandwidth mean shift search step is executed to find the best position, orientation and scale of these objects. Experiments show that it successfully detects the position, scale and orientation of objects.