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Unconstrained Asian roads often involve poor infrastructure, affectingoverall road safety. Missing traffic signs are a regular part of such roads.Missing or non-existing object detection has been studied for locating missingcurbs 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 thesigns 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 firstpublicly available missing object dataset. To train the models for identifyingmissing signs, we complement our dataset with 10K traffic sign tracks, with 40percent of the traffic signs having cues visible in the scenes. For identifyingmissing signs, we propose the Cue-driven Contextual Attention units (CueCAn),which we incorporate in our model encoder. We first train the encoder toclassify the presence of traffic sign cues and then train the entiresegmentation model end-to-end to localize missing traffic signs. Quantitativeand qualitative analysis shows that CueCAn significantly improves theperformance of base models.

CueCAn: Cue Driven Contextual Attention For Identifying Missing Traffic Signs on Unconstrained Roads