  Online multi-object tracking (MOT) is an active research topic in the domain
of computer vision. Although many previously proposed algorithms have exhibited
decent results, the issue of tracklet inactivation has not been sufficiently
studied. Simple strategies such as using a fixed threshold on classification
scores are adopted, yielding undesirable tracking mistakes and limiting the
overall performance. In this paper, a conditional random field (CRF) based
framework is put forward to tackle the tracklet inactivation issue in online
MOT problems. A discrete CRF which exploits the intra-frame relationship
between tracking hypotheses is developed to improve the robustness of tracklet
inactivation. Separate sets of feature functions are designed for the unary and
binary terms in the CRF, which take into account various tracking challenges in
practical scenarios. To handle the problem of varying CRF nodes in the MOT
context, two strategies named as hypothesis filtering and dummy nodes are
employed. In the proposed framework, the inference stage is conducted by using
the loopy belief propagation algorithm, and the CRF parameters are determined
by utilizing the maximum likelihood estimation method followed by slight manual
adjustment. Experimental results show that the tracker combined with the
CRF-based framework outperforms the baseline on the MOT16 and MOT17 benchmarks.
The extensibility of the proposed framework is further validated by an
extensive experiment.
