Target tracking algorithm based on Kalman filter and optimization MeanShift

Heng Wu; Tao Han; Jie Zhang

doi:10.1117/12.2292720

15 November 2017 Target tracking algorithm based on Kalman filter and optimization MeanShift

Heng Wu, Tao Han, Jie Zhang

Proceedings Volume 10605, LIDAR Imaging Detection and Target Recognition 2017; 1060526 (2017) https://doi.org/10.1117/12.2292720
Event: LIDAR Imaging Detection and Target Recognition 2017, 2017, Changchun, China

Abstract

Background change ,shape change and target covering will all cause target tracking failure. Real-time and accuracy in target tracking is the problem that must be considered. This paper first presents the Mean Shift algorithm, then the Mean Shift algorithm iterative weight is modified with main information more prominent, secondary information suppressed, avoiding the tedious root, improving the real-time and effectiveness of target tracking：The target template updating algorithm is present to solve change of background and target shape change. Then a Kalman filter in the horizontal position and the vertical position is established to solve the problem of target tracking completely covered. Simulation results show that target tracking algorithm on the condition of target template update has higher tracking accuracy , higher real-time property and at the same time is robust than the traditional Mean Shift tracking algorithm .

Citation Download Citation

Heng Wu, Tao Han, and Jie Zhang "Target tracking algorithm based on Kalman filter and optimization MeanShift", Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 1060526 (15 November 2017); https://doi.org/10.1117/12.2292720

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available