Semi-supervised Detector Training with Prototypes for Vehicle Detection

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Abstract

Adapting detectors to new datasets is needed in scenarios where a user has a specific dataset that contains novel classes or is recorded in a setting where a pretrained detector fails. While detectors based on Convolutional Neural Networks (CNNs) are state-of-the-art and nowadays publicly available, they suffer from bad generalization capabilities when applied on datasets that notably differ from the one they were trained on. Finetuning the detector is only possible if the dataset is large enough to not destroy the underlying feature representation. We propose a method where only a few prototypes are labeled for training in a semi-supervised manner. In particular, we separate the detection from the classification step to avoid impairing the bounding box proposal generation. Our trained prototype classification network provides labels to automatically source a large dataset containing 20 to 30 times more samples without further supervision, which we then use to train a more powerful network. We evaluate our method on a private vehicle dataset with six classes and show that evaluating on a previously unseen recording site we can gain an accuracy increase of 9% at same precision and recall levels. We further show that finetuning with as few as 25 labeled samples per class doubles accuracy compared to directly using pretrained features for nearest neighbor classification.
Original languageEnglish
Title of host publication2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
PublisherInstitute of Electrical and Electronics Engineers
Pages4261-4266
Number of pages6
ISBN (Electronic)978-1-5386-7024-8
DOIs
Publication statusPublished - Nov 2019
Event22nd IEEE International Conference on Intelligent Transportation Systems - Auckland, New Zealand
Duration: 27 Oct 201930 Oct 2019
https://www.itsc2019.org/

Conference

Conference22nd IEEE International Conference on Intelligent Transportation Systems
Abbreviated titleITSC 2019
CountryNew Zealand
CityAuckland
Period27/10/1930/10/19
Internet address

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Cite this

Waltner, G., Opitz, M., Krispel, G., Possegger, H., & Bischof, H. (2019). Semi-supervised Detector Training with Prototypes for Vehicle Detection. In 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 (pp. 4261-4266). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ITSC.2019.8916901