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.

本文提出了一种名为 FAMNet 的端到端模型，其中特征提取、亲和性估计和多维分配都在单个网络中优化，通过直接从分配标准答案中监督学习来实现鲁棒 MOT 的区分性特征和高阶亲和性模型。同时，将单目标跟踪技术和专用目标管理方案集成到 FAMNet 跟踪系统中，进一步恢复假阴性和抑制外部检测器生成的噪声目标候选项。在 MOT2015、MOT2017、KITTI-Car 和 UA-DETRAC 等多个基准测试上进行了评估，并与现有技术进行比较，在所有测试中均取得了出色表现。