Abstract
In this study, a semi-automatic video annotation method is proposed, utilizing temporal information to eliminate false-positives with a tracking-by-detection approach by employing multiple hypothesis tracking (MHT). MHT method automatically forms tracklets which are confirmed by human operators to enlarge the training set. A novel incremental learning approach helps to annotate videos in an iterative way. The experiments performed on AUTH Multidrone Dataset reveals that the annotation workload can be reduced up to 96% by the proposed approach.
Abstract (translated)
URL
https://arxiv.org/abs/2101.06977