Temporal Coherence for Active Learning in Videos

Javad Zolfaghari Bengar, Abel Gonzales-Garcia, Gabriel Villalonga, Bogdan Raducanu, Hamed Habibi Aghdam, Mikhail Mozerov, Antonio M. Lopez, Joost van de Weijer

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


Autonomous driving systems require huge amounts of data to train. Manual annotation of this data is time-consuming and prohibitively expensive since it involves human resources. Therefore, active learning emerged as an alternative to ease this effort and to make data annotation more manageable. In this paper, we introduce a novel active learning approach for object detection in videos by exploiting temporal coherence. Our active learning criterion is based on the estimated number of errors in terms of false positives and false negatives. The detections obtained by the object detector are used to define the nodes of a graph and tracked forward and backward to temporally link the nodes. Minimizing an energy function defined on this graphical model provides estimates of both false positives and false negatives. Additionally, we introduce a synthetic video dataset, called SYNTHIA-AL, specially designed to evaluate active learning for video object detection in road scenes. Finally, we show that our approach outperforms active learning baselines tested on two datasets.
Original languageEnglish
Title of host publicationProceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019
PublisherInstitute of Electrical and Electronics Engineers
Number of pages10
ISBN (Electronic)978-1-7281-5023-9
Publication statusPublished - Oct 2019
Externally publishedYes
EventCVRSUAD 2019: 7th Workshop on Computer Vision for Road Scence Understanding & Autonomous Driving - Seoul, Korea, Republic of
Duration: 27 Oct 2019 → …

Publication series

NameProceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019


ConferenceCVRSUAD 2019
Country/TerritoryKorea, Republic of
Period27/10/19 → …


  • Active learning
  • Deep learning
  • Video object detection

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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