Abstract. In this paper, we propose an online Multi-Object Tracking
(MOT) approach which integrates the merits of single object tracking
and data association methods in a unified framework to handle noisy detections and frequent interactions between targets. Specifically, for applying single object tracking in MOT, we introduce a cost-sensitive tracking loss based on the state-of-the-art visual tracker, which encourages
the model to focus on hard negative distractors during online learning.
For data association, we propose Dual Matching Attention Networks
(DMAN) with both spatial and temporal attention mechanisms. The
spatial attention module generates dual attention maps which enable
the network to focus on the matching patterns of the input image pair,
while the temporal attention module adaptively allocates different levels
of attention to different samples in the tracklet to suppress noisy observations. Experimental results on the MOT benchmark datasets show
that the proposed algorithm performs favorably against both online and
offline trackers in terms of identity-preserving metrics