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Ensemble of Two-Stage Regression Based Detectors for Accurate Vehicle Detection in Traffic Surveillance Data

: Sommer, L.; Acatay, O.; Schumann, Arne; Beyerer, Jürgen


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Signal Processing Society:
AVSS 2018, 15th IEEE International Conference on Advanced Video and Signal-based Surveillance. Proceedings : 27-30 November 2018, Auckland, New Zealand
Piscataway, NJ: IEEE, 2018
ISBN: 978-1-5386-9294-3
ISBN: 978-1-5386-9293-6
ISBN: 978-1-5386-9295-0
International Conference on Advanced Video and Signal-Based Surveillance (AVSS) <15, 2018, Auckland>
Conference Paper
Fraunhofer IOSB ()

The growing amount of traffic surveillance data results in an increased need for automatic detection systems to analyze the data. For this purpose, deep learning based detection frameworks like Faster R-CNN and SSD have been employed in recent years. Though the detection accuracy is clearly improved compared to conventional detection methods, there exists large potential for further improvements especially in case of adverse weather conditions. In this paper, we employ the RefineDet detection framework as it combines advantages of several detection frameworks including Faster R-CNN and SSD. We use an ensemble of two detectors with different base networks to generate detections that are more robust. For this, SENets - the winner of the ImageNet2017 classification challenge - are used in addition to ResNet-50. To account for small vehicles in the background and strong variation in vehicle scale, we apply multi-scale testing. Our proposed detector achieves top-performing results on the UA-DETRAC dataset especially in case of rainy and nighttime scenarios.