TY  - CONF
AU  - Javad Zolfaghari Bengar
AU  - Abel Gonzalez-Garcia
AU  - Gabriel Villalonga
AU  - Bogdan Raducanu
AU  - Hamed H. Aghdam
AU  - Mikhail Mozerov
AU  - Antonio Lopez
AU  - Joost Van de Weijer
A2  - ICCVW
PY  - 2019//
TI  - Temporal Coherence for Active Learning in Videos
BT  - IEEE International Conference on Computer Vision Workshops
SP  - 914
EP  - 923
N2  - 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.
UR  - https://ieeexplore.ieee.org/document/9022609
L1  - http://refbase.cvc.uab.es/files/ZGV2019.pdf
UR  - http://dx.doi.org/10.1109/ICCVW.2019.00120
N1  - LAMP; ADAS; 600.124; 602.200; 600.118; 600.120; 600.141
ID  - Javad Zolfaghari Bengar2019
ER  -