Multi-Task Structure-Aware Context Modeling for Robust Keypoint-Based Object Tracking

Xi Li, Liming Zhao, Wei Ji, Yiming Wu, Fei Wu, Ming Hsuan Yang, Dacheng Tao, Ian Reid

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

In the fields of computer vision and graphics, keypoint-based object tracking is a fundamental and challenging problem, which is typically formulated in a spatio-temporal context modeling framework. However, many existing keypoint trackers are incapable of effectively modeling and balancing the following three aspects in a simultaneous manner: temporal model coherence across frames, spatial model consistency within frames, and discriminative feature construction. To address this problem, we propose a robust keypoint tracker based on spatio-temporal multi-task structured output optimization driven by discriminative metric learning. Consequently, temporal model coherence is characterized by multi-task structured keypoint model learning over several adjacent frames; spatial model consistency is modeled by solving a geometric verification based structured learning problem; discriminative feature construction is enabled by metric learning to ensure the intra-class compactness and inter-class separability. To achieve the goal of effective object tracking, we jointly optimize the above three modules in a spatio-temporal multi-task learning scheme. Furthermore, we incorporate this joint learning scheme into both single-object and multi-object tracking scenarios, resulting in robust tracking results. Experiments over several challenging datasets have justified the effectiveness of our single-object and multi-object trackers against the state-of-the-art.

Original languageEnglish
Article number8322285
Pages (from-to)915-927
Number of pages13
JournalIEEE transactions on pattern analysis and machine intelligence
Volume41
Issue number4
DOIs
Publication statusPublished - 2019 Apr 1

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All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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