Multi-target tracking by learning local-to-global trajectory models

作者：

Highlights：

• A unified framework to online learn local-to-global trajectory models is proposed.

• The iterative algorithm can alternately update the trajectory models.

• We solve inferences of target IDs for all the detections by using the MRF model.

• Our method has low complexity compared with most state-of-the-art methods.

摘要

Highlights•A unified framework to online learn local-to-global trajectory models is proposed.•The iterative algorithm can alternately update the trajectory models.•We solve inferences of target IDs for all the detections by using the MRF model.•Our method has low complexity compared with most state-of-the-art methods.

论文关键词：Local-to-global,Trajectory model,Markov random field,Belief propagation,Iterative update

论文评审过程：Received 14 March 2014, Revised 10 July 2014, Accepted 13 August 2014, Available online 3 September 2014.

论文官网地址：https://doi.org/10.1016/j.patcog.2014.08.013