  Unconstrained Asian roads often involve poor infrastructure, affecting
overall road safety. Missing traffic signs are a regular part of such roads.
Missing or non-existing object detection has been studied for locating missing
curbs and estimating reasonable regions for pedestrians on road scene images.
Such methods involve analyzing task-specific single object cues. In this paper,
we present the first and most challenging video dataset for missing objects,
with multiple types of traffic signs for which the cues are visible without the
signs in the scenes. We refer to it as the Missing Traffic Signs Video Dataset
(MTSVD). MTSVD is challenging compared to the previous works in two aspects i)
The traffic signs are generally not present in the vicinity of their cues, ii)
The traffic signs cues are diverse and unique. Also, MTSVD is the first
publicly available missing object dataset. To train the models for identifying
missing signs, we complement our dataset with 10K traffic sign tracks, with 40
percent of the traffic signs having cues visible in the scenes. For identifying
missing signs, we propose the Cue-driven Contextual Attention units (CueCAn),
which we incorporate in our model encoder. We first train the encoder to
classify the presence of traffic sign cues and then train the entire
segmentation model end-to-end to localize missing traffic signs. Quantitative
and qualitative analysis shows that CueCAn significantly improves the
performance of base models.
