期刊
出版社
IEEE
DOI: 10.1109/SITIS.2018.00021
关键词
Multi-object Tracking; Adaptive Tobit Kalman Filter; Hungarian Algorithm
类别
资金
- European Project: SURVANT within the H2020 FTIPilot-2015 [720417]
This paper presents an online, real-time, multi- object tracking algorithm based on a novel method for data association. Tracking multiple objects in real-world scenes includes several challenges, such as a) object detectors with low detection accuracy, b) false alarms, and c) unmatched tracked objects. In this paper, we propose a novel filtering method based on the theory of censored data by utilizing an Adaptive Tobit Kalman filter to estimate the object's position with high accuracy. Furthermore, in order to deal with false alarms and unmatched tracked objects, we use the non maximum suppression and a modified Hungarian algorithm, respectively. Experiments in public datasets show that the proposed method outperforms state of the art methods in multi-object tracking with a substantial low computational cost compared to other methods in the area.
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