Orientation and scale invariant mean shift using object mask-based kernel

In this paper, we propose a new method for object tracking based on mean shift algorithm using a kernel which has the shape of the target object, and with probabilistic estimation of the orientation change and scale adaptation. The proposed method uses an object mask to construct a kernel which has the shape of the actual object for tracking. Orientation is adjusted using probabilistic estimation of orientation and scale is adapted using a newly proposed descriptor for scale. Tests results show that the proposed method is robust to background clutter and tracks objects very accurately.

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Kwang Moo Yi, Ho Seok Ahn, Jin Young Choi
• Keywords: Computer Vision, ICPR 2008, Mean Shift Algorithm, Object Tracking, Probabilistic Estimation