Transfer learning‐based online multiperson tracking with Gaussian process regression. (2nd October 2018)

• Record Type: Journal Article
• Authors: Zhang, Baobing; Li, Siguang; Huang, Zhengwen; Rahi, Babak H.; Wang, Qicong; Li, Maozhen
• Other Names: Barbosa Jorge G. guestEditor.; Jeannot Emmanuel guestEditor.; Li Maozhen guestEditor.
• Is Part Of: Concurrency and computation. Volume 30:Number 23(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-10-02
• Subjects: Kalman filter -- multi‐person tracking -- prior information -- regression -- semi‐supervised -- tracking‐by‐detection -- transfer learning; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4917
• Languages: English
• ISSNs: 1532-0626

Abstract

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.