Robust Pedestrian Tracking Using Interactive Multiple Model Particle Filter and Feature Matching

Zhen Chang Chen, Chih Wei Tang

研究成果: 書貢獻/報告類型會議論文篇章同行評審

2 引文 斯高帕斯(Scopus)

摘要

Pedestrian tracking with a single motion model and a single cue cannot be robust against combination of changes of target motion and appearance, caused by either static or dynamic changes in surrounding environments. Different from previous work, this paper proposes to refer to both multiple motion models and cues to improve accuracy of single pedestrian tracking. By integrating the compensated motion model into model filtering of interacting multiple model particle filter (IMMPF) and updating mode probabilities using the state with the maximum color based likelihood weight, the interacting stage of IMMPF generates more accurate approximation of the mixed a priori probability distribution of the target state. Within the search window centering at the predicted state of the constant velocity (CV) model of IMMPF, feature matching of scale- and rotation-invariant feature descriptors helps locate the target. Experimental results show that the proposed scheme is robust against the change of target motion, occlusion, rotation, scaling, and illumination variations. The average RMSE and overlap ratio of the proposed scheme significantly outperforms color based IMMPF using compensated motion model and fast L1-tracker.

原文???core.languages.en_GB???
主出版物標題ICARM 2018 - 2018 3rd International Conference on Advanced Robotics and Mechatronics
發行者Institute of Electrical and Electronics Engineers Inc.
頁面480-485
頁數6
ISBN(電子)9781538670668
DOIs
出版狀態已出版 - 11 1月 2019
事件3rd IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2018 - Singapore, Singapore
持續時間: 18 7月 201820 7月 2018

出版系列

名字ICARM 2018 - 2018 3rd International Conference on Advanced Robotics and Mechatronics

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???event.eventtypes.event.conference???3rd IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2018
國家/地區Singapore
城市Singapore
期間18/07/1820/07/18

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