Strong Interference UAV Motion Target Tracking Based on Target Consistency Algorithm

Abstract

In recent years, unmanned aerial vehicle (UAV) image target tracking technology, which obtains motion parameters of moving targets and achieves a behavioral understanding of moving targets by identifying, detecting and tracking moving targets in UAV images, has been widely used in urban safety fields such as accident rescue, traffic monitoring and personnel detection. Due to the problems of complex backgrounds, small scale and a high density of targets, as well as mutual occlusion among targets in UAV images, this leads to inaccurate results of single object tracking (SOT). To solve the problem of tracking target loss caused by inaccurate tracking results, this paper proposes a strong interference motion target tracking method based on the target consistency algorithm for SOT based on an interframe fusion and trajectory confidence mechanism, fusing previous frames for the tracking trajectory correction of current frames, learning again from previous frames to update the model and adjusting the tracking trajectory according to the tracking duration. The experimental results can show that the accuracy of the proposed method in this paper is improved by 6.3% and the accuracy is improved by 2.6% compared with the benchmark method, which is more suitable for applications in the case of background clutter, camera motion and viewpoint change.

DB-Tracker: Multi-Object Tracking for Drone Aerial Video Based on Box-MeMBer and MB-OSNet

“…where d (1) (i, j) represents the Mahalanobis distance and d (2) (i, j) represents the cosine distance. λ is the weighting factor.…”

“…At the same time, the high agility of drones, when compared with fixed cameras, can make the scope of aerial videos more flexible and diverse. Drone video data are particularly informative in terms of content and time, promoting the growing importance of drone aerial videos in numerous sectors of object detection and tracking [1,2]. However, compared with multi-object recognition and tracking jobs in ordinary view videos, drone aerial videos face several obstacles, such as frame degradation, uneven object distribution density, tiny object size, and real-time performance, which have piqued the interest of academics in recent years generated a lot of interest and research in the world and industry [3].…”