摘要
A simple yet efficient tracking framework is proposed for real-time multi-object tracking with micro aerial vehicles(MAVs). It's basic missions for MAVs to detect specific targets and then track them automatically. In our method, candidate regions are generated using the salient detection in each frame and then classified by an eural network. A kernelized correlation filter(KCF) is employed to track each target until it disappears or the peak-sidelobe ratio is lower than a threshold. Besides, we define the birth and death of each tracker for the targets. The tracker is recycled if its target disappears and can be assigned to a new target. The algorithm is evaluated on the PAFISS and UAV123 datasets. The results show a good performance on both the tracking accuracy and speed.
A simple yet efficient tracking framework is proposed for real-time multi-object tracking with micro aerial vehicles(MAVs).It’s basic missions for MAVs to detect specific targets and then track them automatically.In our method,candidate regions are generated using the salient detection in each frame and then classified by an eural network.A kernelized correlation filter(KCF) is employed to track each target until it disappears or the peak-sidelobe ratio is lower than a threshold.Besides,we define the birth and death of each tracker for the targets.The tracker is recycled if its target disappears and can be assigned to a newtarget.The algorithm is evaluated on the PAFISS and UAV123 datasets.The results showa good performance on both the tracking accuracy and speed.
基金
Supported by the National Natural Science Foundation of China(6160303040,61433003)
Yunnan Applied Basic Research Project of China(201701CF00037)
Yunnan Provincial Science and Technology Department Key Research Program(Engineering)(2018BA070)
