Fast and accurate video annotation using dense motion hypotheses

Fagot-Bouquet, Loïc; Rabarisoa, Jaonary; Pham, Quoc Cuong

doi:10.1109/icip.2014.7025631

Cited by 2 publications

(1 citation statement)

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“…Bianco et al [1] has developed a tool which uses algorithms like linear interpolation, template matching and as well as supervised object detector depending on mode of operation which can be manual, semi -automatic or fully automatic aiding the annotator to speed-up the annotation, allowing the deep networks to learn from a considerably large annotated data. Bouquet et al [7] has attempted to annotate video's by propagating the annotation throughout the frames using an offline tracker followed by dynamic programming and distance transformation to penalize to the displacement between frames. Konyushkova et al [12] has shown a different perspective of human -computer interaction for data-annotation by choosing the best sequence of actions to annotate images in least amount of time.…”

Section: Related Workmentioning

confidence: 99%

One-Click Annotation with Guided Hierarchical Object Detection

Subramanian¹,

Subramanian²

2018

Preprint

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The increase in data collection has made data annotation an interesting and valuable task in the contemporary world. This paper presents a new methodology for quickly annotating data using click-supervision and hierarchical object detection. The proposed work is semi-automatic in nature where the task of annotations is split between the human and a neural network. We show that our improved method of annotation reduces the time, cost and mental stress on a human annotator. The research also highlights how our method performs better than the current approach in different circumstances such as variation in number of objects, object size and different datasets. Our approach also proposes a new method of using object detectors making it suitable for data annotation task. The experiment conducted on PASCAL VOC dataset revealed that annotation created from our approach achieves a mAP of 0.995 and a recall of 0.903. The Our Approach has shown an overall improvement by 8.5%, 18.6% in mean average precision and recall score for KITTI and 69.6%, 36% for CITYSCAPES dataset. The proposed framework is 3-4 times faster as compared to the standard annotation method.

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Section: Related Workmentioning

confidence: 99%