X-MAS: Extremely Large-Scale Multi-Modal Sensor Dataset for Outdoor Surveillance in Real Environments-Reference-Cited by-同舟云学术

X-MAS: Extremely Large-Scale Multi-Modal Sensor Dataset for Outdoor Surveillance in Real Environments

Published:2023-02 Issue:2 Volume:8 Page:1093-1100
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Noh DongKi¹^ORCID,Sung Changki¹,Uhm Teayoung²^ORCID,Lee WooJu¹,Lim Hyungtae¹^ORCID,Choi Jaeseok³^ORCID,Lee Kyuewang⁴^ORCID,Hong Dasol¹,Um Daeho⁴,Chung Inseop³^ORCID,Shin Hochul⁵,Kim MinJung⁶,Kim Hyoung-Rock⁷^ORCID,Baek SeungMin⁷^ORCID,Myung Hyun¹^ORCID

Affiliation:

1. School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea

2. Korea Institute of Robotics and Technology Convergence (KIRO), Pohang, Republic of Korea

3. Department of Intelligence and Information, Seoul National University (SNU), Seoul, Republic of Korea

4. Department of Electrical and Computer Engineering, Automation and Systems Research Institute (ASRI), SNU, Seoul, Republic of Korea

5. Electronics and Telecommunications Research Institute (ETRI), Daejeon, Republic of Korea

6. Kim Jaechul Graduate School of Artificial Intelligence, KAIST, Daejeon, Republic of Korea

7. Advanced Robotics Lab., LG Electronics, Seoul, Republic of Korea

Funder

Institute for Information and Communication Technology Promotion

Korea Government, MSIT

Korea Evaluation Institute of Industrial Technology

Ministry of Trade, Industry and Energy

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Control and Optimization,Computer Science Applications,Computer Vision and Pattern Recognition,Mechanical Engineering,Human-Computer Interaction,Biomedical Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/7083369/9990971/10015866.pdf?arnumber=10015866

Reference47 articles.

1. LaSOT: A High-Quality Benchmark for Large-Scale Single Object Tracking

2. Online Object Tracking: A Benchmark

3. Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges

4. ViViD++ : Vision for Visibility Dataset

5. A Computationally Efficient Real-Time Vehicle and Speed Detection System for Video Traffic Surveillance

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