Abstract—In this paper, we propose the adaptive mean-shift Kalman tracking algorithm based on the mean-shift algorithm and the Kalman filter for tracking a laparoscopic instrument in laparoscopic surgery. With an iterative update of the target candidate in the mean-shift process, the proposed algorithm has improved the tracking performance over a typical mean-shift algorithm. In addition, the Kalman filter is employed to enhance the chance of tracking accuracy, especially when the object disappears from the scene. We tested the tracking performance of our proposed algorithm through simulated videos and real laparoscopic surgery videos. From all experimental results, the proposed algorithm can locate the target object correctly even when the size and the shape of the target have been changed. In the difficult situation when the target is hiding behind an obstacle, this algorithm can still track the target object correctly after it comes out. In addition, the proposed algorithm can be applied to locate different types of laparoscopic instruments.
Index Terms—Mean-Shift algorithm, Kalman filter, object tracking, laparoscopic surgery
Vera Sa-ing and Jackrit Suthakorn are with the Department of Biomedical Engineering, Mahidol University, Thailand (e-mail: email@example.com).
Saowapak S. Thongvigitmanee is with Image Technology Lab, National Electronics and Computer Technology Center, Thailand
Chumpon Wilasrusmee is with the Department of Surgery, Ramathibodi Hospital, Thailand
Cite: Vera Sa-Ing, Saowapak S. Thongvigitmanee, Chumpon Wilasrusmee, and Jackrit Suthakorn, "Object Tracking for Laparoscopic Surgery Using the Adaptive Mean-Shift Kalman Algorithm," International Journal of Machine Learning and Computing vol. 1, no. 5, pp. 441-447 , 2011.