Online Multi-Object Tracking (MOT) is a challenging problem and has many
important applications including intelligence surveillance, robot navigation
and autonomous driving. In existing MOT methods, individual object's movements
and inter-object relations are mostly modeled separately and relations between
them are still manually tuned. In addition, inter-object relations are mostly
modeled in a symmetric way, which we argue is not an optimal setting. To tackle
those difficulties, in this paper, we propose a Deep Continuous Conditional
Random Field (DCCRF) for solving the online MOT problem in a track-by-detection
framework. The DCCRF consists of unary and pairwise terms. The unary terms
estimate tracked objects' displacements across time based on visual appearance
information. They are modeled as deep Convolution Neural Networks, which are
able to learn discriminative visual features for tracklet association. The
asymmetric pairwise terms model inter-object relations in an asymmetric way,
which encourages high-confidence tracklets to help correct errors of
low-confidence tracklets and not to be affected by low-confidence ones much.
The DCCRF is trained in an end-to-end manner for better adapting the influences
of visual information as well as inter-object relations. Extensive experimental
comparisons with state-of-the-arts as well as detailed component analysis of
our proposed DCCRF on two public benchmarks demonstrate the effectiveness of
our proposed MOT framework.

在此论文中，我们提出了一种基于深度连续条件随机场的在线多物体跟踪方法，其中的一元项和二元项分别通过 Convolution 神经网络的训练和非对称建模来估计跟踪对象的位移和关系，该方法在端到端训练和实验测试中表现出显著的优势。