Vehicle-Road Multi-View Interactive Data Fusion

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Vehicle-Road Multi-View Interactive Data Fusion

Zhang, Xinyu / Li, Jun / Li, Zhiwei / Liu, Huaping / Zhou, Mo / Wang, Li / Zou, Zhenhong

Aiming at solving the problem of target occlusion in crowded scenes, the aggregation of multiple camera views can outperform a single view. Some researchers project the feature map or RGB image getting from the feature extraction network to the ground plane by perspective transformation and then use the spatial aggregation without anchor frame to aggregate the feature maps of multiple views. However, the projected feature maps are limited by a lack of semantic information. In order to obtain feature maps with rich semantic information, we will introduce a new multi-view detection method MVT, which effectively extracts features from multiple perspectives. To improve TP and the overall robustness of the model, we will also introduce a method for predicting the positioning uncertainty of bounding box reliability.

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Dokumentinformationen

Titel :

Vehicle-Road Multi-View Interactive Data Fusion

Beteiligte:

Zhang, Xinyu (Autor:in) / Li, Jun (Autor:in) / Li, Zhiwei (Autor:in) / Liu, Huaping (Autor:in) / Zhou, Mo (Autor:in) / Wang, Li (Autor:in) / Zou, Zhenhong (Autor:in)

Erschienen in:

Multi-sensor Fusion for Autonomous Driving ; Kapitel : 8 ; 177-190

Erscheinungsdatum :

2023-05-11

Format / Umfang :

14 pages

ISBN :

978-981-99-3280-1 , 978-981-99-3279-5

DOI :

https://doi.org/10.1007/978-981-99-3280-1_8

Medientyp :

Aufsatz/Kapitel (Buch)