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@ {InProceedings

author = {A. Cortes, O. Senderos, N. Aranjuelo, M. Nieto, and O. Otaegui },

title = {Semi-automatic tracking-based labeling tool for automotive applications },

year = {2015-10-05 },

keys = {Massive labelling, video annotation, video summarization },

pages = {- },

abstract = {The trend toward smart cars continues to build momentum in the automotive industry. As vision based sensors proliferate in the vehicles the quantity and variety of data will become overwhelming and difficult to be processed effectively. Computer vision-based approaches can be trained and evaluated with such data sources once adequately labeled. However, accurately annotating entities in video is labor intensive and an expensive task. As the quantity of available video grows, traditional solutions to this task are unable to scale to meet the needs of sectors like automotive or security. We present a semi-automatic multi-purpose annotation tool which reduces the manual annotation effort by enabling the user to verify automatically generated annotations, rather than annotating from scratch. This tool has been successfully used in a variety of example applications in the domain of Advanced Driver Assistance Systems. },

issn = { },

in = {22st ITS World Congress, Bordeaux, France },