A Video Target Tracking and Correction Model with Blockchain and Robust Feature Location

Yiru Jiang; Dezhi Han; Mingming Cui; Yuan Fan; Yachao Zhou

doi:10.3390/s23052408

A Video Target Tracking and Correction Model with Blockchain and Robust Feature Location

Sensors (Basel). 2023 Feb 22;23(5):2408. doi: 10.3390/s23052408.

Authors

Yiru Jiang¹, Dezhi Han¹, Mingming Cui¹, Yuan Fan², Yachao Zhou²

Affiliations

¹ College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
² Hangzhou Anheng Information Technology Co., Ltd., Hangzhou 310051, China.

Abstract

In this paper, a cutting-edge video target tracking system is proposed, combining feature location and blockchain technology. The location method makes full use of feature registration and received trajectory correction signals to achieve high accuracy in tracking targets. The system leverages the power of blockchain technology to address the challenge of insufficient accuracy in tracking occluded targets, by organizing the video target tracking tasks in a secure and decentralized manner. To further enhance the accuracy of small target tracking, the system uses adaptive clustering to guide the target location process across different nodes. In addition, the paper also presents an unmentioned trajectory optimization post-processing approach, which is based on result stabilization, effectively reducing inter-frame jitter. This post-processing step plays a crucial role in maintaining a smooth and stable track of the target, even in challenging scenarios such as fast movements or significant occlusions. Experimental results on CarChase2 (TLP) and basketball stand advertisements (BSA) datasets show that the proposed feature location method is better than the existing methods, achieving a recall of 51% (27.96+) and a precision of 66.5% (40.04+) in the CarChase2 dataset and recall of 85.52 (11.75+)% and precision of 47.48 (39.2+)% in the BSA dataset. Moreover, the proposed video target tracking and correction model performs better than the existing tracking model, showing a recall of 97.1% and a precision of 92.6% in the CarChase2 dataset and an average recall of 75.9% and mAP of 82.87% in the BSA dataset, respectively. The proposed system presents a comprehensive solution for video target tracking, offering high accuracy, robustness, and stability. The combination of robust feature location, blockchain technology, and trajectory optimization post-processing makes it a promising approach for a wide range of video analytics applications, such as surveillance, autonomous driving, and sports analysis.

Keywords: decentralized surveillance; privacy-preserving target tracking; secure tracking of objects; target correction; trajectory optimization.

Grants and funding

This research was funded by the Natural Science Foundation of Shanghai under Grant 21ZR1426500, and the Top-notch Innovative Talent Training Program for Graduate students of Shanghai Maritime University under Grant 2021YBR008.