Abstract
In order to extract the key and useful features of the target in the UAV video image and strong marking ability, a feature extraction method for the UAV video image based on target tracking is proposed. The sparse beam method is used to adjust the splicing of UAV video images. Based on this, the pixel coordinates are obtained through the frame difference method to detect and locate the target. According to the target detection and positioning results, the video image of the target area is selected and preprocessed by the wavelet transform algorithm Target area video image, and extract the target area video image feature, through hierarchical particle filtering to achieve target tracking, to achieve the extraction of UAV video image feature. The experimental results show that: in the ORL database experiment, the average feature extraction percentage is 78.08%, and the average target tracking error is 1.16; in the COIL-20 database experiment, the average feature extraction percentage is 82.55%, and the average target tracking error is 1.20, which meets the needs of UAV video image feature extraction and target tracking.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, Y., Liu, J., Pei, J., et al.: The risk factors that can increase possibility of mandibular canal wall damage in adult: a cone-beam computed tomography (CBCT) study in a Chinese population. Med. Sci. Monitor 24(2), 26–36 (2018)
Azimi, S.M., Britz, D., Engstler, M., et al.: Advanced steel microstructural classification by deep learning methods. Sci. Rep. 8(1), 2128 (2018)
Ballerini, L., Lovreglio, R., Valdés Hernández, M.C., et al.: Perivascular spaces segmentation in brain MRI using optimal 3D filtering. Sci. Rep. 8(1), 2132–2132 (2018)
Lewis, A.G., Schriefers, H., Bastiaansen, M., et al.: Assessing the utility of frequency tagging for tracking memory-based reactivation of word representations. Sci. Rep. 8(1), 7897 (2018)
Liu, S., Liu, D., Srivastava, G., et al.: Overview and methods of correlation filter algorithms in object tracking. Complex Intell. Syst. (2020). https://doi.org/10.1007/s40747-020-00161-4
Hilchey, M.D., Rajsic, J., Huffman, G., et al.: Dissociating orienting biases from integration effects with eye movements. Psychol. Sci. 29(3), 328–339 (2018)
Jiang, H., Qu, P., Wang, J.W., et al.: Effect of NF-κB inhibitor on Toll-like receptor 4 expression in left ventricular myocardium in two-kidney-one-clip hypertensive rats. Eur. Rev. Med. Pharmacol. Sci. 22(10), 3224–3233 (2018)
Rina, L.D.N., Walburg, K.V., Martin, V.H.P., et al.: Retinal pigment epithelial cells control early mycobacterium tuberculosis infection via interferon signaling. Investigative Opthalmol. Vis. Sci. 59(3), 1384–1395 (2018)
Harris, H., Sagi, D.: Visual learning with reduced adaptation is eccentricity-specific. Sci. Rep. 8(1), 608 (2018)
Liu, S., Liu, G., Zhou, H.: A robust parallel object tracking method for illumination variations. Mob. Netw. Appl. 24(1), 5–17 (2019)
Liu, S., Lu, M., Li, H., et al.: Prediction of gene expression patterns with generalized linear regression model. Front. Genet. 10, 120 (2019)
Barrea, A., Delhaye, B.P., Lefèvre, P., et al.: Perception of partial slips under tangential loading of the fingertip. Sci. Rep. 8(1), 7032–7032 (2018)
Lu, M., Liu, S.: Nucleosome positioning based on generalized relative entropy. Soft. Comput. 23(19), 9175–9188 (2018). https://doi.org/10.1007/s00500-018-3602-2
Acknowledgements
The application of UAV spray in the city pest control service (2019H033-KQ)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Zhang, X., Liu, Zj., Qu, Mf. (2021). Research on Feature Extraction Method of UAV Video Image Based on Target Tracking. In: Liu, S., Xia, L. (eds) Advanced Hybrid Information Processing. ADHIP 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 348. Springer, Cham. https://doi.org/10.1007/978-3-030-67874-6_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-67874-6_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-67873-9
Online ISBN: 978-3-030-67874-6
eBook Packages: Computer ScienceComputer Science (R0)