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• W2939497660 abstract "Data association-based multiple object tracking (MOT) involves multiple separated modules processed or optimized differently, which results in complex method design and requires non-trivial tuning of parameters. In this paper, we present an end-to-end model, named FAMNet, where Feature extraction, Affinity estimation and Multi-dimensional assignment are refined in a single network. All layers in FAMNet are designed differentiable thus can be optimized jointly to learn the discriminative features and higher-order affinity model for robust MOT, which is supervised by the loss directly from the assignment ground truth. We also integrate single object tracking technique and a dedicated target management scheme into the FAMNet-based tracking system to further recover false negatives and inhibit noisy target candidates generated by the external detector. The proposed method is evaluated on a diverse set of benchmarks including MOT2015, MOT2017, KITTI-Car and UA-DETRAC, and achieves promising performance on all of them in comparison with state-of-the-arts." @default.
• W2939497660 created "2019-04-25" @default.
• W2939497660 creator A5061469520 @default.
• W2939497660 creator A5087101031 @default.
• W2939497660 date "2019-04-09" @default.
• W2939497660 modified "2023-10-16" @default.
• W2939497660 title "FAMNet: Joint Learning of Feature, Affinity and Multi-dimensional Assignment for Online Multiple Object Tracking" @default.
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• W2939497660 doi "https://doi.org/10.48550/arxiv.1904.04989" @default.
• W2939497660 hasPublicationYear "2019" @default.
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