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Data association-based multiple object tracking (MOT) involves multipleseparated modules processed or optimized differently, which results in complexmethod design and requires non-trivial tuning of parameters. In this paper, wepresent an end-to-end model, named FAMNet, where Feature extraction, Affinityestimation and Multi-dimensional assignment are refined in a single network.All layers in FAMNet are designed differentiable thus can be optimized jointlyto learn the discriminative features and higher-order affinity model for robustMOT, which is supervised by the loss directly from the assignment ground truth.We also integrate single object tracking technique and a dedicated targetmanagement scheme into the FAMNet-based tracking system to further recoverfalse negatives and inhibit noisy target candidates generated by the externaldetector. The proposed method is evaluated on a diverse set of benchmarksincluding MOT2015, MOT2017, KITTI-Car and UA-DETRAC, and achieves promisingperformance on all of them in comparison with state-of-the-arts.

FAMNet: Joint Learning of Feature, Affinity and Multi-dimensional Assignment for Online Multiple Object Tracking