Open Access

ARTICLE

Adaptive Object Tracking Discriminate Model for Multi-Camera Panorama Surveillance in Airport Apron

Dequan Guo¹, Qingshuai Yang², Yu-Dong Zhang³, Gexiang Zhang¹, Ming Zhu¹, Jianying Yuan^1,*
1 School of Control Engineering, Chengdu University of Information Technology, Chengdu, 610225, China
2 School of Computer Science, Sichuan University, Chengdu, 610041, China
3 Department of Informatics, University of Leicester, Leicester, LE1 7RH, UK
* Corresponding Author: Jianying Yuan. Email:
(This article belongs to this Special Issue: Modeling and Analysis of Autonomous Intelligence)

Computer Modeling in Engineering & Sciences 2021, 129(1), 191-205. https://doi.org/10.32604/cmes.2021.016347

Received 27 February 2021; Accepted 28 June 2021; Issue published 24 August 2021

Abstract

Autonomous intelligence plays a significant role in aviation security. Since most aviation accidents occur in the take-off and landing stage, accurate tracking of moving object in airport apron will be a vital approach to ensure the operation of the aircraft safely. In this study, an adaptive object tracking method based on a discriminant is proposed in multi-camera panorama surveillance of large-scale airport apron. Firstly, based on channels of color histogram, the pre-estimated object probability map is employed to reduce searching computation, and the optimization of the disturbance suppression options can make good resistance to similar areas around the object. Then the object score of probability map is obtained by the sliding window, and the candidate window with the highest probability map score is selected as the new object center. Thirdly, according to the new object location, the probability map is updated, the scale estimation function is adjusted to the size of real object. From qualitative and quantitative analysis, the comparison experiments are verified in representative video sequences, and our approach outperforms typical methods, such as distraction-aware online tracking, mean shift, variance ratio, and adaptive colour attributes.

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Keywords

Autonomous intelligence; discriminate model; probability map; scale adaptive tracking

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Citations