TY - GEN
T1 - GCANet
T2 - Computing Conference, 2022
AU - Peng, Peiran
AU - Mu, Feng
AU - Yan, Peilin
AU - Song, Liqiang
AU - Li, Hui
AU - Chen, Yu
AU - Li, Jianan
AU - Xu, Tingfa
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Pedestrian detection is a critical but challenging research field widely applicable in self-driving, surveillance and robotics. The performance of pedestrian detection is not ideal under the limitation of imaging conditions, especially at night or occlusion. To overcome these obstacles, we propose a cross-modal pedestrian detection network based on Gaussian Cross Attention (GCANet) improving the detection performance by a full use of multi-modal features. Through the bidirectional coupling of local features of different modals, the feature interaction and fusion between different modals are realized, and the salient features between multi-modal are effectively emphasized, thus improving the detection accuracy. Experimental results demonstrate GCANet achieves the highest accuracy with the state-of-the-art on KAIST multi-modal pedestrian dataset.
AB - Pedestrian detection is a critical but challenging research field widely applicable in self-driving, surveillance and robotics. The performance of pedestrian detection is not ideal under the limitation of imaging conditions, especially at night or occlusion. To overcome these obstacles, we propose a cross-modal pedestrian detection network based on Gaussian Cross Attention (GCANet) improving the detection performance by a full use of multi-modal features. Through the bidirectional coupling of local features of different modals, the feature interaction and fusion between different modals are realized, and the salient features between multi-modal are effectively emphasized, thus improving the detection accuracy. Experimental results demonstrate GCANet achieves the highest accuracy with the state-of-the-art on KAIST multi-modal pedestrian dataset.
KW - Gaussian Cross Attention
KW - Multi-modal fusion
KW - Pedestrian detection
UR - http://www.scopus.com/inward/record.url?scp=85135027095&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-10464-0_35
DO - 10.1007/978-3-031-10464-0_35
M3 - Conference contribution
AN - SCOPUS:85135027095
SN - 9783031104633
T3 - Lecture Notes in Networks and Systems
SP - 520
EP - 530
BT - Intelligent Computing - Proceedings of the 2022 Computing Conference
A2 - Arai, Kohei
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 14 July 2022 through 15 July 2022
ER -