Object tracking with shape representation network using color information

Abstract

In this article, we propose an object tracking method using a neural network which represents the shape of an object based on the object's color information. We previously proposed a specific form of multiple-layered neural network which has a suitable structure to represent an object's shape. This network (shape representation network, SRN) originally was developed to deal with black and white images but it is extended for color images in this article. SRN is capable of representing objects of various kinds of shape and color with an arbitrary degree of blurring. Its learning capability enables automatic model construction for various shapes including their color information. To perform object tracking with color information, we introduce Mahalanobis distance in color space and improve the tracking performance. Some experiments are performed to evaluate the performance of the proposed method using real image sequences.