Tiny Drone Tracking Framework Using Multiple Trackers and Kalman-based Predictor

Abstract

Unmanned aerial vehicles like drones are one of the key development technologies with many beneficial applications. As they have made great progress, security and privacy issues are also growing. Drone tacking with a moving camera is one of the important methods to solve these issues. There are various challenges of drone tracking. First, drones move quickly and are usually tiny. Second, images captured by a moving camera have illumination changes. Moreover, the tracking should be performed in real-time for surveillance applications. For fast and accurate drone tracking, this paper proposes a tracking framework utilizing two trackers, a predictor, and a refinement process. One tracker finds a moving target based on motion flow and the other tracker locates the region of interest (ROI) employing histogram features. The predictor estimates the trajectory of the target by using a Kalman filter. The predictor contributes to keeping track of the target even if the trackers fail. Lastly, the refinement process decides the location of the target taking advantage of ROIs from the trackers and the predictor. In experiments on our dataset containing tiny flying drones, the proposed method achieved an average success rate of 1.134 times higher than conventional tracking methods and it performed at an average run-time of 21.08 frames per second.

Counter Drone Technology: A Review

“…The system was tested for tracking multiple aerial and ground targets in real-time. Son et al (2021) proposed a fast and accurate drone tracking system utilizing two trackers, a predictor, and a refinement process [94]. One tracker identified a moving target based on motion flow, while the other located the Region Of Interest (ROI) using histogram features.…”

Small Object Detection and Tracking: A Comprehensive Review

“…In the research conducted by Son et al [ 49 ], a model for tracking minuscule drones was put forth, featuring a predictor based on the Kalman filter and multiple trackers. This model is composed of two distinct trackers, a predictor, and a refining operation.…”

Probability weighting effect in vertex cover of networks via prospect-theoretic learning

“…Over the past decades, vertex cover (VC) of networks has attracted significant attention due to the wide applications in various fields, such as the wireless sensor networks [1], autonomous intelligent systems [2], target tracking [3], and security surveillance in social networks [4], etc.…”