Automatic object tracking method based on SAD and UKF-mean shift

作者：Liu Xian Ru^*; Cai Zi Xing; Tang Jin

来源：Pattem Recognition and Aitificial Intelligence, 2010, 23(5): 646-652.

• Accurate motion detection
• Block matching methods
• Complex scenes
• Current frame
• Image frames
• Mean shift
• Motion information
• Moving objects
• Moving-object detection
• Object Detection
• Object model
• Object tracking
• State-space models
• Sum of absolute differences
• Time properties
• Unscented Kalman Filter

Aiming at the problems in accurate motion detection and tracking location under complex scene, an automatic object tracking method combined sum of absolute difference (SAD) and Mean shift with unscented Kalman filter (UKF) is proposed. Firstly block matching method based on SAD is used to estimate the displacement between current frame and successive frame. Then the disparity cues are utilized to detect the moving object automatically and build the object model and state-space model for following tracking task. Finally Mean shift with UKF is employed to filter and estimate the state of the object and locate the object in subsequence image frame. The experimental results show that the proposed moving object detection method effectively detects moving objects in scene and acquires the motion information of objects. Compared with the related methods, the proposed tracking strategy based on UKF-Mean shift has better tracking results and time property.

• 出版日期2010
• 单位中南大学