  Correlation filter (CF) has recently exhibited promising performance in
visual object tracking for unmanned aerial vehicle (UAV). Such online learning
method heavily depends on the quality of the training-set, yet complicated
aerial scenarios like occlusion or out of view can reduce its reliability. In
this work, a novel time slot-based distillation approach is proposed to
efficiently and effectively optimize the training-set's quality on the fly. A
cooperative energy minimization function is established to score the historical
samples adaptively. To accelerate the scoring process, frames with high
confident tracking results are employed as the keyframes to divide the tracking
process into multiple time slots. After the establishment of a new slot, the
weighted fusion of the previous samples generates one key-sample, in order to
reduce the number of samples to be scored. Besides, when the current time slot
exceeds the maximum frame number, which can be scored, the sample with the
lowest score will be discarded. Consequently, the training-set can be
efficiently and reliably distilled. Comprehensive tests on two well-known UAV
benchmarks prove the effectiveness of our method with real-time speed on a
single CPU.
