COVID-19 contact tracking by group activity trajectory recovery over camera networks

Highlights：

• To address the problem of the inaccuracy of existing GPS positioning for tracking confirmed patients, this paper combines GPS data and video data to achieve tracking of close contact patients and proposes a combined optimization method (CO-SC).

• A high-quality GATR-GPS dataset is constructed to simulate the tracking of close contact patients in realistic scenarios, and it is verified that the proposed method can significantly improve the performance of close contact patient tracking.

• Randomly take the GPS data and video data at the same moment, calculate their errors, traverse them 10 times, and average them to get the average error of the two kinds of data. The average error of GPS data is over 200m, while the error of video data is within 1m.

• In the self-built dataset and all using GPS data, our method obtains 89.80% for Rank 1 and 80.15% for mAP, which is a 7.35% improvement on Rank 1 and a 5.81% improvement on mAP compared to the CADL method.

摘要

•We address a novel task named ”group activity trajectory recovery” and screen suspected high-risk patients in the population by combining GPS data and video data to track COVID-19 contacts in an epidemiological survey. It combines efficiency and accuracy compared to existing methods, and the actual operation is more reliable and efficient.•To address the problem of the inaccuracy of existing GPS positioning for tracking confirmed patients, this paper combines GPS data and video data to achieve tracking of close contact patients and proposes a combined optimization method (CO-SC).•A high-quality GATR-GPS dataset is constructed to simulate the tracking of close contact patients in realistic scenarios, and it is verified that the proposed method can significantly improve the performance of close contact patient tracking.•Randomly take the GPS data and video data at the same moment, calculate their errors, traverse them 10 times, and average them to get the average error of the two kinds of data. The average error of GPS data is over 200m, while the error of video data is within 1m.•In the self-built dataset and all using GPS data, our method obtains 89.80% for Rank 1 and 80.15% for mAP, which is a 7.35% improvement on Rank 1 and a 5.81% improvement on mAP compared to the CADL method.