首页|Real-time tracking of fast-moving object in occlusion scene

Real-time tracking of fast-moving object in occlusion scene

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Tracking the fast-moving object in occlusion situa-tions is an important research topic in computer vision.Despite numerous notable contributions have been made in this field,few of them simultaneously incorporate both object's extrinsic features and intrinsic motion patterns into their methodologies,thereby restricting the potential for tracking accuracy improve-ment.In this paper,on the basis of efficient convolution opera-tors(ECO)model,a speed-accuracy-balanced model is put for-ward.This model uses the simple correlation filter to track the object in real-time,and adopts the sophisticated deep-learning neural network to extract high-level features to train a more complex filter correcting the tracking mistakes,when the trac-king state is judged to be poor.Furthermore,in the context of scenarios involving regular fast-moving,a motion model based on Kalman filter is designed which greatly promotes the tracking stability,because this motion model could predict the object's future location from its previous movement pattern.Additionally,instead of periodically updating our tracking model and training samples,a constrained condition for updating is proposed,which effectively mitigates contamination to the tracker from the background and undesirable samples avoiding model degrada-tion when occlusion happens.From comprehensive experi-ments,our tracking model obtains better performance than ECO on object tracking benchmark 2015(OTB100),and improves the area under curve(AUC)by about 8%and 32%compared with ECO,in the scenarios of fast-moving and occlusion on our own collected dataset.

speed-accuracy balancedmotion modelingcon-strained updater

LI Yuran、LI Yichen、ZHANG Monan、YU Wenbin、GUAN Xinping

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Department of Automation,Shanghai Jiao Tong University,Shanghai 200240,China

Key Laboratory of Systems Control and Information Processing,Ministry of Education of China,Shanghai 200240,China

国家自然科学基金国家自然科学基金Oceanic Interdisciplinary Program of Shanghai Jiao Tong UniversityOceanic Interdisciplinary Program of Shanghai Jiao Tong UniversityOceanic Interdisciplinary Program of Shanghai Jiao Tong University

6237324662203299SL2022MS008SL2020ZD206SL2022MS010

2024

10.23919/JSEE.2024.000058

系统工程与电子技术(英文版)
中国航天科工防御技术研究院 中国宇航学会 中国系统工程学会 中国系统仿真学会

系统工程与电子技术(英文版)

CSTPCD
影响因子：0.64
ISSN：1004-4132
年,卷(期)：2024.35(3)
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