  Recently, many breakthroughs are made in the field of Video Object Detection
(VOD), but the performance is still limited due to the imaging limitations of
RGB sensors in adverse illumination conditions. To alleviate this issue, this
work introduces a new computer vision task called RGB-thermal (RGBT) VOD by
introducing the thermal modality that is insensitive to adverse illumination
conditions. To promote the research and development of RGBT VOD, we design a
novel Erasure-based Interaction Network (EINet) and establish a comprehensive
benchmark dataset (VT-VOD50) for this task. Traditional VOD methods often
leverage temporal information by using many auxiliary frames, and thus have
large computational burden. Considering that thermal images exhibit less noise
than RGB ones, we develop a negative activation function that is used to erase
the noise of RGB features with the help of thermal image features. Furthermore,
with the benefits from thermal images, we rely only on a small temporal window
to model the spatio-temporal information to greatly improve efficiency while
maintaining detection accuracy.
  VT-VOD50 dataset consists of 50 pairs of challenging RGBT video sequences
with complex backgrounds, various objects and different illuminations, which
are collected in real traffic scenarios. Extensive experiments on VT-VOD50
dataset demonstrate the effectiveness and efficiency of our proposed method
against existing mainstream VOD methods. The code of EINet and the dataset will
be released to the public for free academic usage.
