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Semi-automatic multi-object video annotation based on tracking, prediction and semantic segmentation


Jaime B. Fernandez, Venkatesh Gurum Munirathnam, Dian Zhang, Suzanne Little, Noel O'Connor

Publication Type: 
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.
Conference Name: 
International Conference on Content-Based Multimedia Indexing (CBMI 2019)
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Research Group: 
Dublin City University (DCU)
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