Deep learning-based Multiple Object Tracking (MOT) currently relies on
off-the-shelf detectors for tracking-by-detection.This results in deep models
that are detector biased and evaluations that are detector influenced. To
resolve this issue, we introduce Deep Motion Modeling Network (DMM-Net) that
can estimate multiple objects' motion parameters to perform joint detection and
association in an end-to-end manner. DMM-Net models object features over
multiple frames and simultaneously infers object classes, visibility, and their
motion parameters. These outputs are readily used to update the tracklets for
efficient MOT. DMM-Net achieves PR-MOTA score of 12.80 @ 120+ fps for the
popular UA-DETRAC challenge, which is better performance and orders of
magnitude faster. We also contribute a synthetic large-scale public dataset
Omni-MOT for vehicle tracking that provides precise ground-truth annotations to
eliminate the detector influence in MOT evaluation. This 14M+ frames dataset is
extendable with our public script (Code at Dataset
<this https URL>, Dataset Recorder
<this https URL>, Omni-MOT Source
<this https URL>). We demonstrate the suitability of Omni-MOT
for deep learning with DMMNet and also make the source code of our network
public.

引入基于深度学习的运动建模网络（DMM-Net）估算多个物体的运动参数，可用于联合检测和关联，以解决现有方法中深度模型过于依赖检测器、评估结果受检测器影响的问题。通过运用 DMM-Net 构建的 Omni-MOT 数据集，消除了检测器对多目标跟踪性能评估的影响，该方法取得了比传统方法更好的性能，速度更快。