MOT-AS: Real-Time Scheduling Framework for Multi-Object Tracking Capturing Accuracy and Stability

Author:

Kang Donghwa1ORCID,Lee Kilho2ORCID,Hong Cheol-Ho3ORCID,Lee Youngmoon4ORCID,Lee Jinkyu5ORCID,Baek Hyeongboo1ORCID

Affiliation:

1. Incheon National University, Incheon, Republic of Korea

2. Soongsil University, Seoul, Republic of Korea

3. ChungAng University, Seoul, Republic of Korea

4. Hanyang University, Ansan, Republic of Korea

5. Sungkyunkwan University, Suwon, Republic of Korea

Funder

National Research Foundation of Korea (NRF)

Publisher

ACM

Reference19 articles.

1. Simple online and realtime tracking

2. nuScenes: A Multimodal Dataset for Autonomous Driving

3. Jinkun Cao, Xinshuo Weng, Rawal Khirodkar, Jiangmiao Pang, and Kris Kitani. 2022. Observation-centric sort: Rethinking sort for robust multi-object tracking. arXiv preprint arXiv:2203.14360 (2022).

4. Donghwa Kang, Seunghoon Lee, Hoon Sung Chwa, Seung-Hwan Bae, Chang Mook Kang, Jinkyu Lee, and Hyeongboo Baek. 2022. RT-MOT: Confidence-Aware Real-Time Scheduling Framework for Multi-Object Tracking Tasks. In IEEE Real-Time Systems Symposium (RTSS). IEEE, 318--330.

5. The MTA Dataset for Multi Target Multi Camera Pedestrian Tracking by Weighted Distance Aggregation

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