  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.
