Papers › Online Multi-Object Tracking with Dual Matching Attention Networks

Online Multi-Object Tracking with Dual Matching Attention Networks

2 Feb 2019ECCV 2018 9arXiv:1902.00749archive 2025-07-28

Ji Zhu, Hua Yang, Nian Liu, Minyoung Kim, Wenjun Zhang, Ming-Hsuan Yang

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.

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Code

jizhu1023/DMAN_MOT mentioned on GitHubtf report

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Tasks

Multi-Object TrackingObjectObject TrackingOnline Multi-Object Tracking

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Multi-Object Tracking MOT16 DMMOT MOTA 46.1 #24 of 24 Archive leaderboard report
Online Multi-Object Tracking MOT16 DMAN MOTA 46.1 #5 of 5 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Average PoolingConvolutionMax PoolingSigmoid Activation

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