Vehicle Crowd Analysis via Transfer Learning

Yusuf K. Hanoglu*, Bilge Gunsel, Meltem Gulbas

*Bu çalışma için yazışmadan sorumlu yazar

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1 Atıf (Scopus)

Özet

We propose a deep learning based approach to vehicle density estimation that adopts CSRNet, originally designed for person crowd analysis, to vehicle crowd analysis. The objective is to exploit the transfer learning to accurately estimate the vehicle density with an increased learning speed. Specifically, the CSRNet architecture pre-trained on the person domain is fine tuned on the vehicle domain by feature tranformation. This is achieved by end-to-end retraining the network to output the spatial distribution of vehicles in congested scenes. The approach is evaluated on Waymo and TRANCOS data sets while ShanghaiTech data set is used for pretraining. Performance reported by the metrics of MAE and RMSE, and PSNR on different test cases, demonstrate the transfer learning significantly improves vehicle density estimation accuracy, compared to the learning from stretch. In particular, the learning accuracy achieved on Waymo, with a small size training data, is validating the potential of the approach in enhancing vehicle crowd analysis for autonomous driving task.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıICECS 2023 - 2023 30th IEEE International Conference on Electronics, Circuits and Systems
Ana bilgisayar yayını alt yazısıTechnosapiens for Saving Humanity
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)9798350326499
DOI'lar
Yayın durumuYayınlandı - 2023
Etkinlik30th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2023 - Istanbul, Turkey
Süre: 4 Ara 20237 Ara 2023

Yayın serisi

AdıICECS 2023 - 2023 30th IEEE International Conference on Electronics, Circuits and Systems: Technosapiens for Saving Humanity

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???event.eventtypes.event.conference???30th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2023
Ülke/BölgeTurkey
ŞehirIstanbul
Periyot4/12/237/12/23

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© 2023 IEEE.

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