HE, Q., SHAO, X., CHEN, W., LI, X., YANG, X., & SUN, T. (2019). Adaptive Multi-Scale Tracking Target Algorithm through Drone. IEICE Transactions on Communications, E102.B(10), 1998–2005. https://doi.org/10.1587/transcom.2018drp0040
Adaptive multi-scale tracking target algorithm through drone
|Author:||He, Qiusheng1; Shao, Xiuyan2; Chen, Wei3,4;|
1School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, P.R. China
2Oulu Advanced Research on Service and Information Systems, University of Oulu, Oulu, FI-90540, Finland
3Mine Digitization Engineering Research Center of the Ministry of Education, School of Computer Science and Technology, China University of Mining and Technology, Jiangsu, 221116, P.R. China
4College of Computer Science and Technology, Xi’an University of Science and Technology, Xi’an, 710054, China
|Online Access:||PDF Full Text (PDF, 4.3 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2019121648341
Institute of Electronics, Information and Communication Engineers,
|Publish Date:|| 2019-12-16
In order to solve the influence of scale change on target tracking using the drone, a multi-scale target tracking algorithm is proposed which based on the color feature tracking algorithm. The algorithm realized adaptive scale tracking by training position and scale correlation filters. It can first obtain the target center position of next frame by computing the maximum of the response, where the position correlation filter is learned by the least squares classifier and the dimensionality reduction for color features is analyzed by principal component analysis. The scale correlation filter is obtained by color characteristics at 33 rectangular areas which is set by the scale factor around the central location and is reduced dimensions by orthogonal triangle decomposition. Finally, the location and size of the target are updated by the maximum of the response. By testing 13 challenging video sequences taken by the drone, the results show that the algorithm has adaptability to the changes in the target scale and its robustness along with many other performance indicators are both better than the most state-of-the-art methods in illumination Variation, fast motion, motion blur and other complex situations.
IEICE transactions on communications. B
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
This work was supported by the National Natural Science Foundation of China (Grant No. 51874300), the National Natural Science Foundation of China and Shanxi Provincial People’s Government Jointly Funded Project of China for Coal Base and Low Carbon (Grant No. U1510115), the National Natural Science Foundation of China (Grant Nos. 51874299, 51104157), the Qing Lan Project and the China Postdoctoral Science Foundation (Grant No. 2013T60574).
© 2019 The Institute of Electronics, Information and Communication Engineers. The Definitive Version of Record can be found online at: https://doi.org/10.1587/transcom.2018drp0040.