Live video object tracking and segmentation using graph cuts

Abstract

Graph cuts have proven to be powerful tools in image segmentation. Previous graph cut research has proposed methods for cutting across large graphs constructed from multiple layered video frames, resulting in an object being tracked across multiple frames. However, this research focuses on cutting graphs constructed from a prerecorded video sequence. In live video scenarios, frames cannot be layered to construct 3D volumes, since the contents of the subsequent frames are unknown. Instead, new graphs must be created and cut for each frame on demand. Resource limitations make this unfeasible on high-resolution videos. In addition, object tracking requires a method for incorporating the previous frame's object position and shape into the current graph. We propose a method for tracking and segmenting objects in live video that utilizes regional graph cuts and object pixel probability maps. The regionalization of the cuts around the tracked object will increase the speed of the tracker, and the object pixel probability maps will enable more flexible tracking.