  Text tracking is to track multiple texts in a video,and construct a
trajectory for each text. Existing methodstackle this task by utilizing the
tracking-by-detection frame-work, i.e., detecting the text instances in each
frame andassociating the corresponding text instances in consecutiveframes. We
argue that the tracking accuracy of this paradigmis severely limited in more
complex scenarios, e.g., owing tomotion blur, etc., the missed detection of
text instances causesthe break of the text trajectory. In addition, different
textinstances with similar appearance are easily confused, leadingto the
incorrect association of the text instances. To this end,a novel
spatio-temporal complementary text tracking model isproposed in this paper. We
leverage a Siamese ComplementaryModule to fully exploit the continuity
characteristic of the textinstances in the temporal dimension, which
effectively alleviatesthe missed detection of the text instances, and hence
ensuresthe completeness of each text trajectory. We further integratethe
semantic cues and the visual cues of the text instance intoa unified
representation via a text similarity learning network,which supplies a high
discriminative power in the presence oftext instances with similar appearance,
and thus avoids the mis-association between them. Our method achieves
state-of-the-art performance on several public benchmarks. The source codeis
available at https://github.com/lsabrinax/VideoTextSCM.
