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Correlation filter (CF) has recently exhibited promising performance invisual object tracking for unmanned aerial vehicle (UAV). Such online learningmethod heavily depends on the quality of the training-set, yet complicatedaerial scenarios like occlusion or out of view can reduce its reliability. Inthis work, a novel time slot-based distillation approach is proposed toefficiently and effectively optimize the training-set's quality on the fly. Acooperative energy minimization function is established to score the historicalsamples adaptively. To accelerate the scoring process, frames with highconfident tracking results are employed as the keyframes to divide the trackingprocess into multiple time slots. After the establishment of a new slot, theweighted fusion of the previous samples generates one key-sample, in order toreduce the number of samples to be scored. Besides, when the current time slotexceeds the maximum frame number, which can be scored, the sample with thelowest score will be discarded. Consequently, the training-set can beefficiently and reliably distilled. Comprehensive tests on two well-known UAVbenchmarks prove the effectiveness of our method with real-time speed on asingle CPU.

Training-Set Distillation for Real-Time UAV Object Tracking