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Transfer learning‐based online multiperson tracking with Gaussian process regression
Author(s) -
Zhang Baobing,
Li Siguang,
Huang Zhengwen,
Rahi Babak H.,
Wang Qicong,
Li Maozhen
Publication year - 2018
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4917
Subject(s) - discriminative model , computer science , artificial intelligence , benchmark (surveying) , machine learning , gaussian process , inference , kriging , frame (networking) , process (computing) , regression , tracking (education) , transfer of learning , pattern recognition (psychology) , gaussian , data mining , mathematics , statistics , psychology , telecommunications , pedagogy , physics , geodesy , quantum mechanics , geography , operating system
Summary Most existing tracking‐by‐detection approaches are affected by abrupt pedestrian pose changes, lighting conditions, scale changes, and real‐time processing, which leads to issues such as detection errors and drifts. To deal with these issues, we present a novel multi‐person tracking framework by introducing a new Gaussian Process Regression based observation model, which learns in a semi‐supervised manner. The background information is taken into consideration to build the discriminative tracker, training samples are re‐weighted appropriately to ease the impact of the potential sample misalignment and noisy during model updating. Unlabeled samples from the current frame provide rich information, which is used for enhancing the tracking inference. Experimental results show that the proposed approach outperforms a number of state‐of‐the‐art methods on some benchmark datasets.