Semi-supervised Detector Training with Prototypes for Vehicle Detection

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

Abstract

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.
Originalspracheenglisch
Titel2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers
Seitenumfang6
PublikationsstatusAngenommen/In Druck - Jun 2019
Veranstaltung22nd IEEE International Conference on Intelligent Transportation Systems - Auckland, Neuseeland
Dauer: 27 Okt 201930 Okt 2019
https://www.itsc2019.org/

Konferenz

Konferenz22nd IEEE International Conference on Intelligent Transportation Systems
KurztitelITSC 2019
LandNeuseeland
OrtAuckland
Zeitraum27/10/1930/10/19
Internetadresse

Dies zitieren

Waltner, G., Opitz, M., Krispel, G., Possegger, H., & Bischof, H. (Angenommen/Im Druck). Semi-supervised Detector Training with Prototypes for Vehicle Detection. in 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 Institute of Electrical and Electronics Engineers.

Semi-supervised Detector Training with Prototypes for Vehicle Detection. / Waltner, Georg; Opitz, Michael; Krispel, Georg; Possegger, Horst; Bischof, Horst.

2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019. Institute of Electrical and Electronics Engineers, 2019.

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

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. Institute of Electrical and Electronics Engineers, 22nd IEEE International Conference on Intelligent Transportation Systems, Auckland, Neuseeland, 27/10/19.
Waltner G, Opitz M, Krispel G, Possegger H, Bischof H. Semi-supervised Detector Training with Prototypes for Vehicle Detection. in 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019. Institute of Electrical and Electronics Engineers. 2019
Waltner, Georg ; Opitz, Michael ; Krispel, Georg ; Possegger, Horst ; Bischof, Horst. / Semi-supervised Detector Training with Prototypes for Vehicle Detection. 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019. Institute of Electrical and Electronics Engineers, 2019.
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N2 - 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.

AB - 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.

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