Object Tracking Using Meanshift Algorithm Combined with Kalman Filter on Robotic Fish

DOI

10.2991/kam-15.2015.46How to use a DOI?

Keywords

meanshift algorithm; kalman filter; kernel function; target tracking.

Abstract

This paper investigates and proposes an improved Meanshift algorithm combined with Kalman Filter aiming at the shortcomings of the Meanshift algorithm theory as well as obvious limitations of a target tracking for the independent visual robotic fish being affected by the fluctuation of the water wave. First, this new algorithm makes use of Kalman filter to obtain the initial position of the Meanshift algorithm. Then, adjust the bandwidth of the kernel function adaptively in the Meanshift tracking algorithm and use the Meanshift algorithm to obtain the position of the tracking target. Finally, we conduct a real-time tracing experiment on the independent visual robotic fish tracking a moving ball. Experimental results show that: compared with the traditional Meanshift algorithm, the improved algorithm tracks the target more accurately and the trajectory of the tracking target is more continuous. Furthermore, it reduces the number of iterations, make the algorithm run faster and improve the real - time in tracking.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Li Xin
AU  - Xiang Wei
PY  - 2015/06
DA  - 2015/06
TI  - Object Tracking Using Meanshift Algorithm Combined with Kalman Filter on Robotic Fish
BT  - Proceedings of the 5th International Symposium on Knowledge Acquisition and Modeling
PB  - Atlantis Press
SP  - 168
EP  - 172
SN  - 1951-6851
UR  - https://doi.org/10.2991/kam-15.2015.46
DO  - 10.2991/kam-15.2015.46
ID  - Xin2015/06
ER  -