Supervised learning and evaluation of KITTI's cars detector with DPM

This paper carries out a discussion on the supervised learning of a car detector built as a Discriminative Part-based Model (DPM) from images in the recently published KITTI benchmark suite as part of the object detection and orientation estimation challenge. We present a wide set of experiments and...

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Bibliographic Details
Published in2014 IEEE Intelligent Vehicles Symposium Proceedings pp. 768 - 773
Main Authors Yebes, J. Javier, Bergasa, Luis M., Arroyo, Roberto, Lazaro, Alberto
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2014
Subjects
Benchmark testing
Detectors
Estimation
Measurement
Training
Tuning
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Summary:This paper carries out a discussion on the supervised learning of a car detector built as a Discriminative Part-based Model (DPM) from images in the recently published KITTI benchmark suite as part of the object detection and orientation estimation challenge. We present a wide set of experiments and many hints on the different ways to supervise and enhance the well-known DPM on a challenging and naturalistic urban dataset as KITTI. The evaluation algorithm and metrics, the selection of a clean but representative subset of training samples and the DPM tuning are key factors to learn an object detector in a supervised fashion. We provide evidence of subtle differences in performance depending on these aspects. Besides, the generalization of the trained models to an independent dataset is validated by 5-fold cross-validation.
ISSN:1931-0587
2642-7214
DOI:10.1109/IVS.2014.6856452