  Short video applications like TikTok and Kwai have been a great hit recently.
In order to meet the increasing demands and take full advantage of visual
information in short videos, objects in each short video need to be located and
analyzed as an upstream task. A question is thus raised -- how to improve the
accuracy and robustness of object detection, tracking, and re-identification
across tons of short videos with hundreds of categories and complicated visual
effects (VFX). To this end, a system composed of a detection module, a tracking
module and a generic object re-identification module, is proposed in this
paper, which captures features of major objects from short videos. In
particular, towards the high efficiency demands in practical short video
application, a Temporal Information Fusion Network (TIFN) is proposed in the
object detection module, which shows comparable accuracy and improved time
efficiency to the state-of-the-art video object detector. Furthermore, in order
to mitigate the fragmented issue of tracklets in short videos, a Cross-Layer
Pointwise Siamese Network (CPSN) is proposed in the tracking module to enhance
the robustness of the appearance model. Moreover, in order to evaluate the
proposed system, two challenge datasets containing real-world short videos are
built for video object trajectory extraction and generic object
re-identification respectively. Overall, extensive experiments for each module
and the whole system demonstrate the effectiveness and efficiency of our
system.
