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Applied Science & Technology Full Text (H.W. Wilson)

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Applied Science & Technology Full Text (H.W. Wilson)

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Applied Science & Technology Full Text (H.W. Wilson)

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Learning efficient single stage pedestrian detection by squeeze-and-excitation network.

• Authors : Ding, Lu (AUTHOR); Wang, Yong (AUTHOR); Laganière, Robert (AUTHOR)

Subjects: PEDESTRIANS; OBJECT recognition (Computer vision); CONVOLUTIONAL neural networks

• Source: Neural Computing & Applications. Dec2021, Vol. 33 Issue 23, p16697-16712. 16p.

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Pedestrian detection with LeNet-like convolutional networks.

• Authors : Cuesta-Infante, Alfredo (AUTHOR); García, Francisco J. (AUTHOR); Pantrigo, Juan J. (AUTHOR)

Subjects: PEDESTRIANS; ORDERED sets; COMPUTER vision

• Source: Neural Computing & Applications. Sep2020, Vol. 32 Issue 17, p13175-13181. 7p.

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DSM-IDM-YOLO: Depth-wise Separable Module and Inception Depth-wise Module Based YOLO for Pedestrian Detection.

• Authors : Panigrahi, Sweta (AUTHOR); Raju, U. S. N. (AUTHOR)

Subjects: PEDESTRIANS; CONVOLUTIONAL neural networks; COMPUTER vision

• Source: International Journal on Artificial Intelligence Tools. Jun2023, Vol. 32 Issue 4, p1-30. 30p.

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Person re‐identification via deep compound eye network and pose repair module.

• Authors : Gu, Hongjian¹ (AUTHOR); Zou, Wenxuan² (AUTHOR); Cheng, Keyang² (AUTHOR)

Subjects: *INFORMATION networks; *INFORMATION retrieval; NEURAL computers

• Source: IET Computer Vision (Wiley-Blackwell). Sep2024, Vol. 18 Issue 6, p826-841. 16p.

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ER‐DeepSORT: Pedestrian Multiobject Tracking with Enhanced Reidentification.

• Authors : Liu, Haiying (AUTHOR); Zheng, Taiheng (AUTHOR); Sun, Fengqian (AUTHOR)

Subjects: OBJECT tracking (Computer vision); PEDESTRIANS; COMPUTER visionJAPAN

• Source: IEEJ Transactions on Electrical & Electronic Engineering. Mar2023, Vol. 18 Issue 3, p427-435. 9p.

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Applied Science & Technology Full Text (H.W. Wilson)

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A pedestrian trajectory prediction method based on improved LSTM network.

• Authors : Liu, Fugang¹ (AUTHOR); Duan, Songnan¹ (AUTHOR) ; Juan, Wang¹ (AUTHOR)

Subjects: PEDESTRIANS; LONG-term memory; JUMP processes

• Source: IET Image Processing (Wiley-Blackwell). 2/7/2024, Vol. 18 Issue 2, p379-387. 9p.

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