Vehicle Crowd Density Estimation Enhanced by Video Flow Maps

Pedram Yousefi*, Bilge Gunsel, Yusuf Kagan Hanoglu

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Özet

This paper proposes an approach to vehicle crowd analysis in video sequences that utilizes flow map extraction based on density maps. We adapted the CANnet2s deep neural network, originally designed for human crowd analysis, to the vehicle domain. In particular we trained CANnet2s from scratch using Stochastic Gradient Descent, where VGG16 backbone trained on ImageNet is used as the pretrained model. Our primary motivation is enhancing vehicle crowd analysis for autonomous driving, hence evaluation of the network is performed on Waymo dataset. We annotated Waymo data with seven attributes, such as occlusion, pose change, bright, dark, blurry, multi-scale, and low density. Performance is reported by MAE, RMSE, and PSNR metrics. Comparative analysis with CANnet, the still-image-based counterpart of CANnet2s, demonstrate that the proposed approach outperforms the still image based learning at all attributes and achieves significant improvement in blurry scenes.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığı14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Proceedings
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)9798350360493
DOI'lar
Yayın durumuYayınlandı - 2023
Etkinlik14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Virtual, Bursa, Turkey
Süre: 30 Kas 20232 Ara 2023

Yayın serisi

Adı14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Proceedings

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???event.eventtypes.event.conference???14th International Conference on Electrical and Electronics Engineering, ELECO 2023
Ülke/BölgeTurkey
ŞehirVirtual, Bursa
Periyot30/11/232/12/23

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Publisher Copyright:
© 2023 IEEE.

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