Object Tracking by Asymmetric Kernel Mean Shift with Automatic Scale and Orientation Selection

Tracking objects using the mean shift method is performed by iteratively translating a kernel in the image space such that the past and current object observations are similar. Traditional mean shift method requires a symmetric kernel, such as a circle or an ellipse, and assumes constancy of the object scale and orientation during the course of tracking. In a tracking scenario, it is not uncommon to observe objects with complex shapes whose scale and orientation constantly change due to the camera and object motions. In this paper, we present an object tracking method based on the asymmetric kernel mean shift, in which the scale and orientation of the kernel adaptively change depending on the observations at each iteration. Proposed method extends the traditional mean shift tracking, which is performed in the image coordinates, by including the scale and orientation as additional dimensions and simultaneously estimates all the unknowns in a few number of mean shift iterations. The exp...

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Alper Yilmaz
• Keywords: Computer Vision, CVPR 2007, Kernel Mean Shift, Mean Shift Iterations, Mean Shift Method, Mean Shift Tracking, Traditional Mean Shift