An Efficient Multi-Task Network for Pedestrian Intrusion Detection-Reference-Cited by-同舟云学术

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An Efficient Multi-Task Network for Pedestrian Intrusion Detection

Published:2023-01 Issue:1 Volume:8 Page:649-660
ISSN:2379-8904
Container-title:IEEE Transactions on Intelligent Vehicles
language:
Short-container-title:IEEE Trans. Intell. Veh.

Author:

Shi Zhenyu¹^ORCID,He Shibo¹^ORCID,Sun Jingchen¹,Chen Tao²^ORCID,Chen Jiming¹^ORCID,Dong Hairong³^ORCID

Affiliation:

1. State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China

2. College of Information Science and Technology, Fudan University, Shanghai, China

3. State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Control and Optimization,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/7274857/10024686/09761812.pdf?arnumber=9761812

Reference49 articles.

1. Context Encoding for Semantic Segmentation

2. Are we ready for autonomous driving? The KITTI vision benchmark suite

3. End-to-End Object Detection with Transformers

4. DAB-DETR: Dynamic anchor boxes are better queries for DETR;liu;Proc Int Conf Learn Representations,0

5. Pedestrian detection algorithm based on multi-scale feature extraction and attention feature fusion

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4. MF-ID: A Benchmark and Approach for Multi-Category Fine-Grained Intrusion Detection;IEEE Transactions on Automation Science and Engineering;2024

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