Semi-automatic multi-object video annotation based on tracking, prediction and semantic segmentation
Refereed Conference Meeting Proceeding
Instrumented and autonomous vehicles can generate very high volumes of video data per car per day all of which must be annotated at a high degree of granularity, detail, and accuracy. Manually or automatically annotating videos at this level and volume is not a trivial task. Manual annotation is slow and expensive while automatic annotation algorithms have shown significant improvement over the past few years. This demonstration presents an application of multi-object tracking, path prediction, and semantic segmentation approaches to facilitate the process of multi-object video annotation for enriched tracklet extraction. Currently, these three approaches are used to enhance the annotation task but more can and will be included. in the future.
International Conference on Content-Based Multimedia Indexing (CBMI 2019)
Digital Object Identifer (DOI):
Dublin City University (DCU)
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